{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
      "Done extracting features for 2000 / 49000 images\n",
      "Done extracting features for 3000 / 49000 images\n",
      "Done extracting features for 4000 / 49000 images\n",
      "Done extracting features for 5000 / 49000 images\n",
      "Done extracting features for 6000 / 49000 images\n",
      "Done extracting features for 7000 / 49000 images\n",
      "Done extracting features for 8000 / 49000 images\n",
      "Done extracting features for 9000 / 49000 images\n",
      "Done extracting features for 10000 / 49000 images\n",
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      "Done extracting features for 12000 / 49000 images\n",
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      "Done extracting features for 40000 / 49000 images\n",
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      "Done extracting features for 44000 / 49000 images\n",
      "Done extracting features for 45000 / 49000 images\n",
      "Done extracting features for 46000 / 49000 images\n",
      "Done extracting features for 47000 / 49000 images\n",
      "Done extracting features for 48000 / 49000 images\n",
      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(1000, 155)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_feats.shape)\n",
    "print(X_val_feats.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
      "d:\\love\\cs\\cs231n\\xiaocong\\assignment1\\cs231n\\classifiers\\linear_svm.py:90: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss+=0.5*reg*np.sum(W*W)\n",
      "c:\\users\\xc\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\numpy\\core\\fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "d:\\love\\cs\\cs231n\\xiaocong\\assignment1\\cs231n\\classifiers\\linear_svm.py:90: RuntimeWarning: overflow encountered in multiply\n",
      "  loss+=0.5*reg*np.sum(W*W)\n"
     ]
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      "lr 5.000000e-07 reg 5.000000e+06 train accuracy: 0.073286 val accuracy: 0.069000\n",
      "best validation accuracy achieved during cross-validation: 0.425000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9,5e-9, 1e-8,5e-8, 1e-7,5e-7]\n",
    "regularization_strengths = [5e4,1e4,1e5,1e6, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rg in regularization_strengths:\n",
    "        svm=LinearSVM()\n",
    "        svm.train(X_train_feats,y_train,learning_rate=lr,reg=rg,\n",
    "                  num_iters=1500,verbose=True)\n",
    "        YTrainPred=svm.predict(X_train_feats)\n",
    "        print(YTrainPred.shape)\n",
    "        YValPred=svm.predict(X_val_feats)\n",
    "        Train_accuracy=np.mean(YTrainPred==y_train)\n",
    "        Val_accuracy=np.mean(YValPred==y_val)\n",
    "        results[(lr,rg)]=(Train_accuracy,Val_accuracy)\n",
    "        if Val_accuracy>best_val:\n",
    "            best_val=Val_accuracy\n",
    "            best_svm=svm\n",
    "        \n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "svm_test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.412\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set: you should be able to get at least 0.40\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iO7RWuG7vZ4eZhoEIWKZNpEFJiKEjJPLYs2sfe/bsYvv4CEbkkXVMoi1jRFpI5nKMjI9hOx36BgosLnf5FXZodNrkx8bYe/DglQm4SIfZ7UsT3/cQKwKtsUwTT9XQnoH4iijw2blnG1+b/gpfO30K7XRIZRKopMudpQlKZ9oMFnawzcrTmV+lMHQdQcvpmQ4xBNHdXTEXZUsQQHQ8VLSAKSBa0BoiubCLRsc3QdcgMrq/je4NG3FCeOhwltX5B3DUKSKvTD6Rxk3laVbP06rP4pkOmYyLNkwS2RSq3kSFSYbyZWwT0laeKPJolI+TKV2HMlw0Dga9ZCzGuKbKO2F/Q3lrNCrS3dkPQjEuKvNY0AEtKHTXutPY+hvKO+4tQWlQgNqA8o5CwQckEgwxqTcaHHvyCfBqtBt1Ws0WrVaLxdUV2u02zVYLwwvQQUBDRSilsVwbrSBKxkJtaBOiJJbZuzCiIVcsUg0UtXpEWwueZ7JabbLc2cbWzBHQJs/fRs7Lo6+vSLVa5hOf/DjJVII33vOWpynhKIqwbRvf9zEMA8MwYku0a6FrrS8q+gtKHrio9HqFFoORrTt4/dvezn0f/yj5oTGuP7CDIBJuWfP51BdWeOrsIjfu2cFSpU4YCW7UpN72QQvv/OHvZfuObTRbTT7wVx9GaZ9Sfx5TIAx6HxRaIFKCGA6iQmwBbBelIubm5vHCFiNjozjNDPlMCgHarQaqU0erENNKIiKYpknacUFHeH4b13UwNnD4pCbmpWkY2EBkRGRSDpapuOOlN7Fv315KuQwzZ59CtoxRKpRYnJokmc6Ry2XR0iREYVoGW7dPENlN1lbWmLjhxSwtr/RMR2SEaCLE0GgJwfZxbIdCySHsJMmFBo1pD6MvT6vkkorGGF7aT6V5hj7S+LkEJ8JpVC7LgD1BfztDKjGA/9Qyfqb3yUzEINYIAkLX2jYQDAwxMAyJlTexKtZiEAlE+oLiVl2DT6NVrNVFpGsf9S6nkWpTWXyYsHGUYsqnWa/jukm0n2YguwfHFkRcmq0ypqPQXohh+NiGycRYHq/eJCE5lFNl+tSX2HkgjZ3diZIUBtKzvXZNlXfateieVgnElp1Go5Um1EBXgTebTbxAE4mLaVokEy6GIaSi9jeUg8QdpbWBtgw2Enid/8rvY9ouBjYiBg3HYvr+EBFFFIWEYRgveyONm0jGlrgfdJdYGttxsYMIMQxKhQL5Qh7XTXB++jzFYrF3hugAnbqeT33oYzxxdJ6hgQSGVWR6pcXnTt7A668PGXVOcMH8vrD8Qww0IVo0ogXRBqERm/ym7nb+BvT9TTcdxLZNgsDnC1+4jze98a0XlbOIEEURvu9jWdbTFDVw8T7TNFEqpuFC2QWXS68oJOrsOTjGjTe8mNfcO8ZDH3uE/r48zTDN97+pn9e+6laenJylszrHzfsnqDbbnJlr8Nbbb2bvRJ564LBcaZPNuMzMLVHIp8i2NO3mGq0NWN6T56Z42W13kcnlqdcrKK9BItfHyXNzfPzTX2bPrlEeOXKEwuA4/cUcqYTL6uw8E0N9FHNZCskcKop5Efh1QGFZFp7fIZNM90yH2C79KXCsgGLOobro46Rchka3s3/XLkYGBkmlDNLuDpQ/xmBpkODG67lxehonaXP40L9yrnqCVDHDFx56mJfdejPF0a187aGvUhocuDIBXdz23eOc+nqZpDnAwuo0B16RYPKJBvvvGOfoz36GkxLw+XPHMIaPYL14lJtefgfNr93H9Lk2XtbALreYtOY4453kdTcWWa673FY8yNiigzbcKxPQhTYsjCjCMA3EMLDEwDIMTBSOgCEGygSrq2MSOkDW5ghKE4SiUF0zXClNy1T4QKDBQMcumB5RXW1z/NFPMZa3sRwFRj9KLJIpk7o2MJRDMpEn0T+BZafRAm1fk0xlcc2A1fk5mq010kmTglR5/Cu/y96X/hDZ4degJMKkN55cU+VtOerCIgdRoCQg0hBpECtLrRMyv1ZldmGRdDqN62qUH5ByOxQKKXJpC1EBhkBkdBWYErQ2YANHeEehg4UBNNBAEJqYlkWAIpSuDz2KQAleoxErsHWrgo7v0fbapFIprj94I7fddhsDA/3UalWazdaGeDI3Ncf9Xz2ElSgShppcup9OYFBZ6zC1cj39Y1PY2kPQaIldTBgdtDIRFfv+IomXsSK6q9iNCyvEnrBlyxa+/OUvMzw8zNpamTAMMYynr4TWW9Xr3SemacbWYdcKj6LoaZb3RibVseIY5yaP0graFPqL7D2wn87KKq1GjebaEo50eM3tL+J//MUpsqkszUqN4cEiB/ePEbbK1Ctt/KCKOZTF84WOFxApRbFUotFeuDIBXYjl4iRTKAwwLSwnieEk+eKDjzE6sQfbjc8KCEKF70c062s8cvQkoXWAA+lxihLR7jS6PtgQFQWYFtiWTRj1vrkxa0dk7Yjx0TSlrMOxJZvAh8HBYQaH+kkkHExT01cq4Vgm2WSKaqXGjp3b0WJQq13P2XMztEIIcZk8NcWB7aNUVtfwVe8rkbn5JWrVNoFhoqVBra6IzCozJzwWx0MePPQUJxfmqJw5RfGvCtg/9wuYrs3s/CLNVZvXbNtPpZljLZXj+OoxjtdbLLWXGZ3vp9AZ5u3c0xMdhgEmBhC7T1zTxDIESwRLYusb0ViGiWhI6ojAtYgME0cJWmJjUYkQiSBKY2gIY3u8Z36ceHKS8uwymXAAI+GSypdwsg0atXmc5AC+ahFEChU62KFgORa2mcRrdzAMk2TC4fzMGbxEyFihD/FnOXP009zY/2oi08DscRFwbZW3xQWHFQZCZNhx8MB2OTuzyuTMIna6wI69B8lmkmggaSawjIDyyhyRWLiWQBR2lztd61sMrnAg2dPgGz6WbaKiC+/QmCaEgSLoumZ0d5KJvWIGFw4nExGGR8cZGR5h9+7dvPXNb2ZkZISE64IOabc3kM4rwpnT57ET/eSyGaKojtY+tp3CEZ/5ZpG1aJhh49yFB0BHKCOLSuwFNw/eIkbrPIG7BwmWsaK5njv/ArZu20mz0SEKIxKujdduk8/niSJNFGkMK/bXRlF02QCl1vpi2XrFfsHF0iu2TOyjcSJgfGyIbDZH86n/RbVZZWW2iQ7btGiRW57nzNwqAmzpz7Bj1xiOCWsthVYRKlRoBdlckrbvcWZ6gfGhAvlM6or1X0Amm6fjh+RTWQzLJZ10efzEU3zxKw8xUOhj+1iOfCHFuVPnKGeSDA0MkCoNoJwkynRpttbQXIyOobXG9wPSmSSddqdnOrYWHXLFBK7ZJGy1qJYrlMZ3kc/10ddfwhBwbZOkA45p4FgWhgGGYRJFml279zN57jxrlSr7DyZYmj7DzNIyo6NDjG/t/YwqrVz6i3nmZqfZdn2RpdMLbL++wPTpKQ51ahyfm6fjeUjgcfTwY5w6cYyxkSGiMGK+skantIWMkyYTDlFf7ODYIUcakzy6dIpSq8jb+Y2e6LBFsEyDeLQLTldpWyLd+IBgECs1EUhoAyvhoAwDU4Pquk2UCFoMTNHYaDqGoDZgeR89doTrClmQJmsNn2q4hf0jw8wtnGXQNWg3fBrRAqPDWZQK6bRMtOGBaWGITUCDSnWJerlGXiJKOYPJucOgPLSkevbgXFPlbWIRnwUjiJtgpVxjudJgdmWRse072ZHuw/c0rDVoN5votE25sUoUeWzbNooyDMpr8+QSiW4A00YbXWt0A5E910hiKFAEKK3wdOyQ8EP1DeWjYysW4vnGcSwyuSK7d1/H297+o/SVCqQSLqlMmjDQtIIOpqmQjQQ+RLNYjoi0wvdniJRNIu2TTGdx0luotFymyy9lqP8MOlJoO0u0+/chMQaGRdiJCHxF2/P5h4/9E+lMntu2nmNn+pNYG5jMBofGqJQbDL5kgGI+xZHHD3Hnna/A0MJaucLiygLbd2zDcZyLSvoC1ivrC9kn67ERy/vjf/desslh+ovfTdRWNCprBGFEuljkqQePMb49Q2S6vP93f4l80kKHAdXVCo7lMDy2hfm5J3ALQ3RIEikhmcygdciZ8wtsGe39bKikk8Jr1/GSCQaHtvHZz/4jX/rao/zcz/04//m/vI/9O++iXm2TG97G2toa0jEZ6RtDggSL9SbppBAFEX7QJpNKYpomjpmi2fAQy+6ZjjfctJ2/O3yc7aMlBkoFBicsdh7YT7FQwnUs8vkMohS2oUk4FqlkAsMSGo0G7XqDeqvBWrPF6ekpCkMjSGInC3NT/NC993Du6Ime6eg0lllaM/FVQM1f4o7aXWS/pHjyyRoPf/pjLFaXCJVCR4ozZyZ54LOfxnWTLM/PABEfn36cnVt30m73Y3kLNKM6rWyRqhtwerb3/9jOioWFYKIxDY0jxJY3gBFHLA1DsLrre8NKoaYnKYwNEdiglKCVRhmAEmwVx8zcUKE2EFjP5YY49GSdW19aZM+BcX7we/+U0cFB7nn9KPb1i0yePovjWlg45PPDpFIF6u1lTMcEhIXFKeyEReAppuefor8vw2gRZk9/mm177oEe3SbXNM/bspKI4RAEwvJqjSDQlIoDlPoG8cMAr90h6TikLANXNJbA4cOHOTV5mpOTp7FsF8tJECjQYqO1SZyNYmwoKyPSHiERkdJEKu5U34uXkRetSkMwuh/TNEgmkwwPj7Jrz14mJsYYGR5k29Zx8vksjm2hVUTH8+h4Gzl/yOD0VI2sWee1N6+iNEQRoMEyLSJgttZPx91CkL8RPf4zhO52lBJUGFEuV1hdWWVxYZm52QVOnTjJl48ksFAXE6V6QRAEZHM55ufnUUozOXmKVqsBojh58gSNRgOt9UXL+plwaTbKuoPme8Ldr3sLZqrG5In7OH/+JIlckWQ6STKZYG2lRitKEoqD77eoN5u0PZ9ms4llQTqdIsQmm05imzaWhDgmmKaNgbC0vNYzHe1Opxub0RgCjx89wdJajUB18MIWsyvzTOzcRiGfx7ZtXNeh0qgzv7zM4lKZThQSmSYBFkGoLwZ6m81mnJnSIxw3S6QTJPJD1D1h5649jI4MYVnQabdoNxtI5NNuNgmCiBDBSWdw0lkUQqvlMT+3zOnzS0zPzrG2ukw2l2fy5FmSqSuevnoR5UqT/mI/RTfNwHA/xbLL8eNnOLEwz0qjjK8UouKxE4UB5bVl5mbPEYUepmnSWKsRVBuUtMPW4nZ29d9MvzFCeWEJw+pdBdki2CI4Ai4GpmFiS+w6McTGFgOHeLyapoUWi5RhYqkQ2zSwpesnFwNLwDbANgTHMHE2oECuO3CQI6da3P/YGtWWzc6dAyzNLXD00VnqtSqN5ipCQCqRxbFstPYwDY1oReB71OpV/DBCGya1dpN6s00mkWD6qS/hNc73TMc1tbwrdY9W06fRaNEMQm6+8SCdtke96ZHIZ/HcAMt0cPHA1OhclmwuR6VWplytsTw/gwRN/HaT4bEtOIZBGPggijjnpDdo4sBkHIDs6n4xQZ7u3zUkDsIpFTEyMsbevfu45ZaXcN2B/WRSCaLAp96oYWRSrK6uEnTA83sPjIHB8uIq+4ZO8qobLe47srW7igBfRWhCKp0CC/KLFEuDuJk+dOjjhxoxYHmlhed5lMtNnFSaeuUcT5734WXGhgKWhmGwb+9eHnzgi+zaNcGUnieZSnLXK+5mbW2VHbt3XAxeXsg0uZyFfaly3+juXclEDO3fifIkzgSy0yQck3KnQml8kERxFK0Mok6d0E0SCQRBhUbVIFFyKQ32YRkRntdm78QgtpOk3gk547dJOb2LuuM4mKaB1gpD4NCRY5QDOHHyJLlSifMLy2ytNZhbrJJMJpmdm6Ovv4AhLq2OohNF5HJ5mrUOFhGW9jFNE8u2CYLe5eOR87OkcgNoXPKFAmtrDTLZLPlcniAMWFlZoZNI4DgO7dUyjU4cb/E8j6bnY9s2hVQaB2HmzBTDg33k8kXCSo3ieM8Hb2ImTIarJaxEHjsT8vjM17l/5TwzfoW278V558R7EqIoZGFxjlqtiuNaOK6DX2/RqtZJY9NqZ9i94zpKVpMHHruf5Hi2Zzps0ZiAqxUuBhEKVwtiCK5AJAoTE43GEDN28TkQGIKBQYRGCSitUEj8W38ja61XbN/13JcWAAAgAElEQVS1hdzwfh468iQN8yg/+Qvv5Ogjpzl9+BBryy0K+QK5bIYwamBambjvTQsxLVwnRafj0fCbuAlFs17HsZOMDmTx1o6zOPUoWwu9/R3BNVXeXz00iWUkQAwarQqDieNkbcGqrNE3XMAuFMEwUY1ObFVFAXfffTcRmkaryisPjFDI5xDL5a//9h+pLc4xMTFONueyEeVtWbFFjTZin7sIYmhMDAzLIpPNMDQ4xF0vfxUTWyYYGRlm5459WK5DvlRkZXGR6XqVc6dP8cUvfJZas4HYFq96zT2Y1gb++9gyOfLI3/ATv5wjnYhIuS6GZWEYFoEfxlkcKNZaKRzPoBPV0BGUKw3Wyg1mZ1cRQ1hcLpNOO+wc3cXXD5/gdG2InbkNBOhEuPfN9/L44a/heR12bJ/g8ccf44N//de8972/y759ewm7gcj1Chx4msK+sCnnwj0bTRX0/YBCbpAgCAm9iFSuiBM2qa5U2XvTPvJ9AzjSIQwSTE7PsriyhpTPIIaJTg+RzA2QHenjlt1befWd/4FMLotfWeRjn3mEJ08+2x+YPB07du6h06zhpkJWa0ssrVaYnK+ige9+zT08+MBX+fyXH2J4eIwoUmSzGdpNn+17d+K1mrStkHK5zULV58CuCYygSavdAGUixuX+2OXyeHKpztjABI5ZIJnppzr9OOVaDcdxmJudY2hogFCbmCqeGBwnxerKCtValU6rw8z0NCsrc2ivSeRrZmfLVOshpV3befj+h3umg4pL/zQcWzvB5/7l00jhBpabZYJ2DVOBFWlMxySbzbJ9+zamp6bYuWsnKoL5+QWUASudBkYyxYLZZPV4hZX5Rf787b+Pp2o9k5EzAjIhpK0IjAQ5q0ZRtalSoPLx99F80WvI77udc1/6OJXaImG7hpMfZe/r9hIoiJSBUqCMOJqltEEkGlMHYPY+bnN5m/f8zu9x/MRTfOITf8f//defY7R/iDtf/3a+/Mn3c+vtAzgjLlFUZnVNY7o5bNuhslZmcvIk58+dpTjQh2QTNLw0yxXQJ2d40XUvpVqZvTIBXVxT5V3IpEgn8wSRJu0KW8fHKK8sMTAwQtRqozRk+kqIm6fdWMZrVtCSxLBsiukk6VyetXINzy+TT7r0bdtKIuGgow3tjMG0zK5fWwGC2c31NC2Llx+8kUw6xYtuvpkX3XEXhpvEcV1sx8UUYercaY4+dojz504zN3uelUoZcRPk0zky2QwjQ0O9E6IV33eXyUA+Ah3hJlxcJ4lpWfHEoiAIFF4nxPMUEoTUqx6VWoOnTs/TbgWIGCyvVBkvzTBc6qPUl+bkQj8TmWV6967C6OgYg4ODVKvLLCzOs7K8RqmvxPnp89x4880Y61wgWmscx8HzvG/aebk+K2Wj2SZCBBJg2w6WZdAx2/hRB9EhOooImkukkjbHp0P++YtPoMMOE9kOgQLlWuSTVXJGjc62LchTj7AYQtKJGC6k2fE9r7oyAV102i3EtEhncihTeMlNBzk9/XlW1+LlbrXeREcac3mVrVu3UigU2Ll9G0nHwlAhViJDo1YnkR0k0zfMaMahMT/F1OQZMtne9wGM5vtQXpsHHniIfDpDuTKPryIS11/P8EgJwzSxHJdkJkMmlSaVdKlV68zOzHP66EkKA1nakU+53SBTHEEsl7Vag4VGm6bXe9bL7LEyXjqk6tYYKI0xudLAVsJqq0lgaOwozjLat38/N1x/kMCPmJjYQRho5hcWEcukGfostyrctP1mRpL9hKkaO+ou9Ubv8pFEUZSAJBFBpMlQp0iTtAUzM48jQ2OUOw2evO+DRFEDVMT19/47DNPGVBFauqtsFSckqO5mnjiBZQNyKhaZdIbr9u+nkPsR/ugDf8bk5DmOPfQhtmVNZqYrTOwcod5so7SJtjx8vwNGiB+1MJwkYWRSr0fUGgadRgPVB198aJrJ6TL/sce/krmmynswn6JeqVMa6EenDI4fO0az3cHNFYmiCkM7JghURNbJk034YBr4tkEhn2V8ZBBPR0xOzdCoVMm7NpGOILoQQ+7dZ5VKZlAqQnXPXzcizXX7r+Mtb72XgYU1WuVVyqfOkrz9VSgngRgWs7PTVBYXOD8/xfHDh2l7Daq1Mro0QHF4gka9zfZt28k6vW86QAk/8qYEGQcIYajk4GkXx3EIfTPeIRZqGo0WtaqFZQszMxW8KGB6dg3HtdGRptn06RudJ+dE5PN5ZlZtaluy9F2RgBgaTSKRYGhoiNnZsxQKHkpFOE6CI0ee4PaXv4L+/n6CILionKenpxkaGvomV8mlKYYbUd651BiGmQRtEUUdaup+DCtBIpGkNXMMvAx2rsinPnOU5bqJY7TJWCnEdglbwnDRIpMpsbBUZV/OY+r8DO1AsRblcTq90xEFHm46h5vI0PHbvOzF1/Pw1x9jfnWZh7/2KH5oxL7dcplMJsO2bdtIZlKkkwnC0KdSi6jX4mMd/E5ExwrwfT9ObQ16H3KuYXJubpZiKc/c+Rn6B5IEUcC56TluecmLcJwUpmUy1N9PMpGk1aiRcGzSyQRrq8vk+rKMjkxQaYSs+QodQdKyqHQC6hvgR3utjqFNnpqbxp4YIpxZIVQ+tWYDQ0MkEblMmjvueDlra2U6nkekNOlsFjFMTIQg8Oloxc7MMJ4fsW94H/NTx1iN6tzaKz+UJkubVNgBTJLeCkZUJ+uWqeqA12zJE6gG9/tzOCKoUFEY3gJGAotWnGpLd2fmBVeJdHPKNpJbC1iWRSqVZGLrdt71Mz/Dpz/1ab74iU+yvDjN7j0j1CoNktkIwzRRkU+r3WZ1dYVqvUW5FREaHu1Wk2w+Sej7LFYiHn9imv6hl/dOw4YovlrUK2wdG2FmZYFKtcYNO3cw4ibohLC0WGFpbpVKJ2BibBuF8VFy2ifVN0bStRHl86X7vhBvj+1a2heW7lEUsRHlPTI0QKVcBdIIwtDEEL/8K7/E2tl5HmhMYbtZqqHL43/+QQaGcqTSCaZOn6DZbtJot2g6aexkFmNwB/Pz0zz82CESGpbf+hb8bO8+PBSUEubF76+84yBPTAprNQ8xAuptj3K9jWVFBMrDdExOTM7S6ATMLs2zfWyceqtBuVVl35Zl+jJljuVuZm61wMmVEb6rRzJMLTjJBHfd/d3c/8CDuMBgYZDV1Trnz0zzm+95Dzff/GLe+c53EIUhYRRx+uxJSn39sS/ciEAbGJgoHfsbNQqRjQUsz55/CsMEy0iRTmaJvBbl5TlWFmZo19Yo9JXoH5/gB19nc262TqPWYHGtSrltk8oWufHGbWT7t5A0A754aJEgMGm0faxEi6RUe6bDNhSZ0gDVpo8rwo7hDL/9Kz+OSILf/oMPUG5WSadzFIoF6vUaDz74IC+Wl3LD/r10ogDTSlKp10jaBqYWlpcqhNUapYEs/gZ2WHprs2S00FpZppiySJkWSzPzHDxwkInhIbJ9/TgJk2zSIQo6PP7o/SQSDgOFJHv2buV73ngve07dgP7o3/PUmePoyGfbzi2cPLdIPp3rmY6FTpX3Hv8LcBMMLpuslRdYrSwxvziH0M3YcF1SaRfDKPCmN93DkSNP8vDDDxFFIb4f4SgHWVlh6vjjrC6t0rf3ZhbaATrROz+KRAy2phlQ8zgqpFpbJZXJ0ap4fNdrv5evP/hVVleX2L7tAOfOnkBpRSLVhxMplDYJrQCJEoh4aG0gWmEojUZhPoddzLZtY1k2u3bs5Kf+jx/n+h07qTx1iHvvPcDswmd48PF/JsJBRZow0NRrEY1awGAuwUifkB4v0Gw28ZXNwnLAe/7L+9m9/3t6rv/aWt59fbgJi+sP7KZSb1FeXCKTClmrtjDNJLPzC6i1ZQZKfRgSkC8WcBIOUehj6AjfC7s7+gyiSHEhWUapbmpfjyjlc4S+H2/wUZp927bjalheWCRTLBFok/NnT5CxTBqTJ0k4JnNrq0S2iVguqWIftpOkWqkxe/QUfYU0L7nhBhKuS3sD2QRAd/NNfBxANleikA9YqTQIlY8f1vHaLTpBlpU1aLTa1Ootap02fthEKUUYhgRhh/FCQNpqsCO7yHz5ACEb2KYPhGHIxMR2cvkSjUabZCKJYViMjA3jJlwefeQh3vLmN9HXV2JuZZFEwsF1bMJAY1qCjuLNE984GCjODd+Iz/v63d+DwkdFEZHyme18iVZ1mU67Sf/4DlJ9WwhCxdjWLaRzDU4ePYOPSXk5Yq3WxG80qZvLeIk8hmkSBvEBRYHfIqF6V1YKjegAlCJE4SZSXYvN5O47buHDn/o8fhCQycbb4IvFEpOTp+kr5NkyNsqRI8fwPR9HGwwNDNNcM1he1nTabex075N7wrUwtE064TC3tEDS6WP7jnG2jQ3ht2qkhodwEw6WAbVahbXlRcbGRjBFc+utLyWfL5BwEyyuVVhZa6FDxfYdOYaKHdrt3jeThUpTsyMmSqN0WgG1+hpr5WW0joP+qVSaPXv24roWyWQO0SZPPvkEjUYdpTSCEEYRgY5YKa8wmErz5Ookw9YQKdX7Dks3akGzQqczSyghzXoVL2iC6ZJ1k4xNjBFaNpVKh0R2mPzIbhKlCULirfSOVnGqYDcbS6PQKAytsTaouy/uMNbdzVe0OXjzzfzd1z5PpVJmZGiUyIsI0fjBhV3bLiODJe6+dSuev0qjtkZ/oYgfGYyNuiTdJiqqY9KbjFxT5W1YFqgIx4BsJk0uuRVRsFqZotasE/htfC+kXVlFh2nsdIrm0hJbRgaZPHaCKIrzsDWKUGm0iv12WscR5F5hGgaubZNIJjBNi2I+w/mVeczxfvzJKdqdAFdpOu0OUbuKkXZpYmFnCriJDK2OT/nsFKvzc4yPj7Jv725e/KKb8H0/jl4/FwhoHZDMJQlDn06nQbO1QqNeYSQaorLQwA807aBNO6jSaC0RhGO0O1XCsMXUWo50os11Wya5f/omyo0NbNMndndksxlGR8dYmpuh3okDdLv2bcN1XPygxRe/9DnuvPNO5uansS0TpSIQA0MEbQoqurBTLf5cyEDpFZX6GTp+i0CVMbQLIpiGYNoumVI/TjJBrTyPneqj0W6TSCXprLVo1lepN5pocz+moTk13yZZb+LaFpYFzXYLpXv38TqJBKHfwbDs2L+fyBBFGqV8br5xP0+cOs/RM7PkclkGBwdJpVJMzS1w+NBjBK3dpLM5UgmHQsrGMBSh18A0NY6TIZfrPd98cn6Fm6+7Adt1SOSSTJ9f4Lbb70D5bQg6SOSBMjENoVmv47V9vFa8q9TrKEzLpFDI0z80zJlz8wyN9tNWFrZroKT3fjG1yVBhFFNZNDtlWu0mvt/Gdix0S5NKpRgfH8cPfDqdNplklhtvPIhpmszNzeH7AbobKVxpNtB2xJnKCq/aOoKyejcyrKhN2/PwKhWIGjS9NlanE6cguw5D/UUKpREOHz7OWGGcF939NgIng1Y+BopI2SjRoA1MwvhMJAwc1EbyHZ4OAVMMXNchVyhQ6TQ4PXWGFx90wAMlJkoJIoqdO3dx8w0voTz7AG2/SmBErFZqeL4C20GdfZT+8VtIficq74WWx1Y3jxXGCnSlXsartchmsmT6TEbHC/iNOv26hWWlWa02OHjTHg49fD/nJ49jBg4KRSixNSAqJl+zseX54OAgfaV+jImtuMU8ba9N1RlgLajxRGON2XPTZPqGaC8sYZgONSsFRZNWu8P5M+doLSyRzeXYPraV22+7vfu+Eq1G87kxRms0Bto7zvx0gakzj+AkCjRqazS8KpVKOfbRY7G8Nk2lPUNt4TEm7YBOpLEQ/tPf7iXJNO/9wQW+78D9fPX8SzZEQhRF5HI57nrl3fzpX36IpGtgmxEf+shfMtQ3hGu7zM2f4+y5kzzyyMO86+ffjSlgGkIUehim052AVLw9X+IzJKINbAf/+3/8MOmCze7hnUhkYjkpEqkUhRK0yqtE9RXagUdlao6nZlvMLNTwI0GHDbYNJkhnTMQyGUg1+JdDJe6YmMKwEtiZwobGZjKRI5EsYFgWlXIZx07R1m1q9TIPP/Iojx06jJEuUqvVsCyLZDLJy297CXMzU5SX5zkzdQ7lt7npuj2snE8SNdZIOSG5wXEso9QzHTffcQdtb5XDJ06we9subrztFmptj21mkYWZ84xObCXl5Gm3qszNTHF+psLRp86ye88OJsYn+KV3v5s9+/bxPa+4nc78OVbKFU4dmWXXDTewfXgDk7tlkzYLVMsrzM2eo9lqkkxlaLWbWJbFli1bKBVLuE6CKNREWti1dx+JVJpjR4/y5JEn4/fokLPtZc77BrZr8h+n7yNtuPT6Z5KJ9jzYFg1PcAJhZbmONj18b4lSMYdjlzGSebaPjrJ7eC9hMoelPERZaFFEeAQSoEjg6IhIx5OYY3qocGP/D624YDjGcR3LTKCsJj/wI9/F8Yc+zKOPhWRyOzn85Bqnpsr8zm+8kVI+w/TMWXRyiMVVj9mFOqvVNWw3TatjsMPWvHQDW2+ureXtOLiZDIZtYWiFIULfYD9nz0xj42JGISnTJJNJERqKoXyaWqXG/NwiRBCqeMETouONOl2ft1Z6QxZvsVik1mjiFvNUgZnFJRrhIRzXZaVSphX5tFYWUQQkbYug02b1/DTV1TKiYdfWHSQSLvl8gcHBQUqlEplsFnuDB2Q9HYLXXmR1xaPVXsRxs4Rhi8D3iFSTSEV0/IBKc5p7b/W49fu28S+PzfHJhw3ETJBIK16+t8PW/hYiJ1kNNxA45RtZIlu3bWPr9p2cmTzFyGABQ/JsnZggnytcTP9z3QRff+Rhxoa2smPHbmwzPuvNtuMMlAvHn154b68YHitiBwl0kEQpjakC3GSSSFL4NNAqiDMFvCl2DCXJZ1IYSlPI9ZOyLILQQzspkimH2WYKn3kSjoVIgo0cn+A6SdCCYycoFEpUlz3K5RpT84tIMk9+cJzlapPFxUWq1SrVapVSPkNfoUBlaZ7rt29joJBlpD8PHZ+0m8K1EgShQTvs3fc+PNjHidNLjAwNsbo4x3XX3cDZc3OcPNumUXRxiyfZYVmMFrKEKqLRbNLsNEmmbbT2Gervo1Iuc1pPUQl8aoFPvlhgZaVCJ+jdvWfbLq1mnVqtQr3VwLJN2p3WxR21lmVgWpBIZBBxME3BrlZJpVIk3MTTZCBQEaGhiIIIw7Gpb0BpBotP4ToJXCNCKw9D+SjRmKaGqIXGIwIakUfSnCKdHUA3V1FoTMfGliRt1cIx+1AGKLFRYmL7Hma4MXfnxRWmNvGCFl6j+f+z9+Yxl1/nfd/nLL/t7vfdl5l5Z+PMcEgOKZISLVmyJMuyZCdWUrtJrLp20jotirRNC6NBgKAIUtQtWjRwkC5BHCBt49pxazu2ZceSKlm2JEeMRJEiqSGH5Owz777e/f7Wc07/+N0ZMQlQ3FcoWBiYB3iJIcH73md+y3POeZ7vQuAXbGx9k35/nXgYsXLqY5w6tc/Vd/6QMAw5POoxji3rOxtsbh+wfzgkqi/R7Ug2tiw/8+/9eSrR3NQZvKfF2/MkvXSECOs0KiEmr+OQ5DYn0hG2KPvXslJFC0PVjdnc3CXuJ3iFI0WUQlbWUjjxUN7RWnusohlFEf3BgPtXr3K7Wz6Uwr6OtCCEw0chdU5hcoZxwrjbZzzoUatUmWm1uXjxcer1GrVanTAMiaIIKWWJKPg+irdD4jAMxnX6/QFxkjDjVzBYkqRLt7uOcAVZPubDFzr8xBVD20/xnoevfmfE1S2Pjz6nefxEB60VrhBcnpse1/zuWF1d5QMvvMDJlVVuvPVdWrU2WWrIM0MUBRweHlKp1Oh2jvjC5/85Z89e4MmnLhDVWlTrTaIwpCgeYJmPR+CVWUZY9ShEn2q1Tdp36DBE5KWutgxn0GYWW4zRYcSyDpCqShDWSAdD4sIR5AVZUnDQSxi5NtUQlD//sMU2TVhVyh+ZPKFWq1GMqyipONzvEtaa/Pt/9a9w9drbfOOrXyXPHUKOeeuttzi3sshMtcml0ytoCZ50pFmG0BIhPUxmUGL6PLzaDKfOPI7tHzA43GWc9tnr7BD5C6xV5/jcF77E+fsbfOwDz6J0wOJCm2T9kH6nQzMMWTt1gmvX75CYIyqzM5y7dJGZsMbvfeGLODl9kbBOMhwcMo5HWGfI8oy8yB/CRMv2mAInCfwQIUsVxWajSbPVBN5F2BICbMn/LRzHaqup0R5FLIlkRkHO+YUZ8DwMoIucOC9AGXr9Q8JaSLN7g+Gwg/ZAhQGRWCDMe2TRHL3coL2IoFpHpwXCHXdWZSnfXOgd3Wfz7nVOrja48c43cOMxw37IsDB4ssWl06fY2dri4HDA9u6AG3dvkhcCYyOyos3evuXkqSc5e/ZpnJse4Pve6nkrwbjfRZkMqhUirRFK8fGP/ABX37rHzNIyIh+hQwHSpzW3wBf+4EWyLMc6SI3BOIdxltw5rDVlv/uYxVtKSatZ44VLj3F3nPErv/E7FIM+xTBmPBhhiwLhLIHnl6SZwOPM6TVazSYzzSZn1s5SrVbwfI8oDMmylCSJ8bR8V+GaPoQoONQ/xndefZOPnN/hs8+P+OWvD3jqhONn/90ELd4k0hIpc/QDPXMnODcn+Yd/vcp/99tVDvpVLqwOwCYIp6mq4zA9eciabLWa/Ob/+U/x8XFpTD7cxahNPD+g0WhQq9Xw/RBhDWsnV9m4d5dvffOrxIWj2x+wv7/PiRMn+MxnPsPHP/bJh4igaeLkhVViO0TKHFHkhAtLjLIuctTH8wXCkzi/TnvtWYRSSF1n2B/hlMGEPtloiBf6HPRTnpzbYTO/yFpzROFqBI3p2xXK90iTDJNZsjwnjmMee+w8504t8MWvfJnOrW/SSEY8tbZAkhviJOHZEzOcWW0xHo3xFAxHfYYmY3VxgsjRPr5wCDt9kTjq9rj7xjV8N+S5Fz6A70J++MmnScZjfuuLX8FvL/DGH3yFw8198mRcPqtRm2s3dnjn3jbXX/42J+bm8LQkzyw3bt4j2dvlJ194jI3x9M9HZhzJuEec9EnzlMxkZEWGtZYwDFEqwNN1tCeQEoTwqNWqSCHQnod1BiFLMaiZWo1nLl3mrevvcGgyyKZ/b+/efpN64DHTrNCuhazNNEtNFRFQwTLILL0koxH4GNOl0rtJMRxMetuWjcFLnJ5vcFLMk48GxGlOnBb0i4xq1QN+dupc0lEfZ3Ly/i69d36fS8uLvPLHv8vGm332NzOCWoM/93M/yrU/+i0+8cQJDm+PuHW/w1t3DuiJWaSK8IMGzzz1SX765z/JlWeeR0XesSCL72nxTsYxF86dLjUZAK1VaR/m+8y0ZvCko8gts/NzxJkpcbbxeCLRCsaCwVE4N9ElKdslztpjtU0ajQZZmhL5FU77AcX+AeNBnzwrEEiUEkjpoSMPISEINasLJ5idnWF2dpZ6vUbg+4RRSJ5nWFvgnCNNimO1CR6EkIL1wyo/+vRdlhoZnsqpeiMsOa3AoMW7xbdKYhEODIbluuVHrgi+8GqNqlcOY+CBv8j0obUurcuk4C/91L9F/6hLMhpxtLfJOEvpdnt0Oh2G4wQpYHMrodMvsMbi+QovDJmdnSWOY3q9Hp/73Of4yIc/fiw971qlQkO1cVh8USeWKVLFdFyGPdJYJ7DCI7M+ngpwKsL3EtLcsrd3wFy7jXMSY2B5PqCTWEbjPn4Y4o7Re8+yDE8HCGcfAsiUUkTVKu9/5knitCSU7R+MSAqLdYJ2ICniHoEnGYwHBIFGKo3DYm1piVbkCcpOPxfp7W3h24K1M2s0ZxfovLPN3Tdep9lukZQCP1gnuHX3HsIZbt2/T5EZhJB0h0ecafgsN0KqvuLW1pCDgx5zfpW12QbNhel1xeuNJvvWkGUJWZGRpxnCCZTS+L5XFmldzjmUUpjCoCaWaXGSIjVU6xFF4ZCeoFGP+MTTL/Dr3/w6Wk7fzhqnGUWWEVQiXGKI0pwsSQm0AQmjNGcwTJgNA6KqRskCAo1VCmE1o1FCMbZYHWNweL7CWEfDambC1tR5OODq66+xt71BnS6V4j7h2ipa1tFuhStPrbF24VmqgeXKlXOIUZ9f+p9+g1T79E3I8oWnmJ1b5PTZC/zoj/0Es3NLeJ5fDlOP8dq+tzhv4XFw1CMKfOqNOqN4RBBVME6Spym5TahXFEL6NNp1ch7QWCdUVmcf6hEY5yjMg7bJ8QaWxhiCIOD69beJixibxQjhJoVG4PselUoF7Qm01iwsLLC0uESr1aJarU5cOx7oh5SfS9P0oePM9OEeIuuy/mvU50pl4VHuUfVzUisefMWDf5RFeXKPJy6fPHnC8I3rFQJPlDYw30c8pLgj+MyP/yhZlpEkCd1ulziOGQ6HD3u8w+GQV199lavX3qEoCur1Op6vmJ2doVKpkOel7sbnPvc5PvOZz0ydw0zjTClTg4dSPj4ZQeBxtLUB2kNJgTY+cZaBFKgsRSnN/m6X0aDH8tICKvCp1eucPFuhc1djigztS9wxRpZRFGFygclztPbwfB9jCxJniSo1tKdwJqe6HILUpIVFS4/D3RwR+KSDEZ6WIAyVKGTQHVC6ABTUgukXEbo9nrl8kZmlOofbm8zUAlaXG7ggRB2FHN7bwmUj5mcjDo6OcKFHsbuL5yuiLKefKITJUK0V5EGGzWIMAamTZGZ6Pe/WTJs0zcnznDzLEELg+z5aa6RUCCmw1kxOhKUWvpucjLPBCC0VvueBnLwfuWGx1irvtTrGTKTdJi8MpnAUVjKwCuVFBFoShB5V3yPOC9bm2jQbEcM4Zn52hmGS4UtNezZjIaoSaMdobAhCRZ57DFP90MBhmjDGsLWxzltXX+Nk2zJX6/HiS2+wvp1z8dIHWVleInaKq6+8TLx3jZXFFQ7Gc5x937O88NTjPP3sCzRaLVozs8wvzpSyEoLv6fBPeUne0+J9Y/2Q5dRRr1Y46AIepfYAACAASURBVMecPnOKOMvZP+hAPqZd13ieodFcZVQIfuf3PoczBdYqCifJTVEWbRyFLQu6s1CY4xVv3/c5ODhklIy4v3GTxbkmzkrCsEK7NYcf+BOvQYHneTQaDebnFxBCEEXRpF3jSJIE3ys9DJWSZFl6vJ63KCVkXQEfWLvO//zlH2bY6/F/f/3L/O3/9IO8cjNBPnDLefCZd/36B445cw3D1t4Gb69LPnhJQuEmSuTTxbvVAJ1ztNvthw45eZ7j+/6/Mlso9akzut0uvV6Pb33rW6yv32dzc4PhYITDYQrH5z//eV5//XV+9Vd/dao8PBFRmBSlLYKMMPQZjyJOXPg4S3Mdrt/c4vr1+yS5Y6ndI6o32N7ukAwOObE8g1edIWqsMnuiydH6K1zd0PzJNcmf/fAQ3HG8Ix1BVMGGVYwpyKVBSZ/BaIBFIGWIMQp0jpQQSEjyFOGXZiHKJAR+RJYVjDp7CJOWRU4IpJpeze/udgdVm+FWp4uwki999xrnzi5jigGGjCtXzrI0E3D7ZheZ+SSjmNgLadbrPP/+58niAZtakA0TWvMR7/fmODc3z5ffuE2imlPnoSsKr9LEdvbJ84wzZ85Qr9ep1cq/S6NRw9iYPM9x2mM0GmGMoSgMjfk2tVqLIHHMtur0OwMqQ48iHqGFPNaJ+Weeu8RWmqAo8dXXDw6J8wJbiagpR+B7PHZykfMz8yTJkMwa8uERa60WnlTkxuLMkLgYszDTxJkxzpc0JUTB9HrvmUn5+Cc+zfsuX+bg3hvc393l1OMf4cQHZlGeT54b6pWQo+t/zKAecX97n1/81X/AyunLSBUSKu9d/q6lFeT3c2J/b3feWuCFAakpLcTub2xy2OmgtMfaXJMs7bN64hTWKe7cucN4HE92mm7S2y5h9WUhmey6cQ9dbqYN3y93ZwWSMKxz5lQFJwRa+bSa8yAEvldKzlrrqEbVyS5DTtT1JGmaTmzBxMQt3WKtZTw+npMOULITrWN1ps+NruXsfJOFhSbLh+PSHef/ZTctAOsEe/sx33wz4YOXygfi+41/Xav7gRP8u4WorLXUarWJTO4Sy8vLjEZDdnZ2uXHjOkppiqKg1WpRqUz/Utzb/S5LrctI51HkBYFusDBfFvSharHQ7fFynHJwNKYuM7TnMej3makqomoTlI/QATpsUGu0aFYLbu36KK+KCqOp8xiOMsLQw1pQkglT1IBwE7u3Up3O2Qf/bpFYQt9DC02ReDTrVayNyOIBXli+rOPxkNwOp85jd++As5cus723iXKSbpxya++I1dVlBgd7yLVFOkdDyCQLtTrNWsDbecLMyipHoxjZP6Rx9jJ5vo/IC+bbLY52djnopyR6epTH6qkTIDXaD2g0mqytnaZSiahWqxhjCEMfz1Olh+RkkZdS4nseS3MLPD53kiVVIQsy9sMWKzOLeJnDHFPWIi9yxllBFHjgSqcrZx15nnE0MMzVQhyQWYfWAb6AUysrREGANZbBaJEUg6CFyLxSldRZxvERcTK9QFbgB6QqIJch+0PDCx/7NDPLZyhs6W2vlA95zL1+n0Equfi+j3L6/ONY4aOlj/pXJCVKc/XvJ8T3D217FI/iUTyKR/H/V7ynZgyP4lE8ikfxKP6/iUfF+1E8ikfxKP4UxqPi/SgexaN4FH8K41HxfhSP4lE8ij+F8Z6iTV57a889QC04KRBafc8bke9B1dwDmVQcyrhSQsBanJIPITVqwqy0E+SKtZZnHl+Yam77H79yy70w/Fucau5z1OvjCosUZ3HRDa53/xqXTlyl2v11WsECb3ZnCecqpJua1cUDArnN0aDB9eI/4NnHv4nZ7zDb6nF4NMed8fv47txf5395fH6qPP7RL/688zyB7+vS+VqFaCXQEjQWnEOKEq6oVHmtAqUx1lFYS5xZjDU4DC7LiK3PZt/wI3/+53js/GNEYX2qPP7+3/7PXXcQc/3WbWq1OucWFlBaIrXiay9fRSvNyYU5fGEIogjCiHg0YnVhAWktmXNs7u9TWMf51QVMYRgmGbd3DmnUavyP//ifTJXHJ//Gh5zn+Xjaw/c8vDAgUJqKDkD4hGEdKXzy4hDPtwgBWtXQ2i99AnXpG65FqbMspURPzACcc/y1P/M3p8qj2ao5rTU4R57nmKIoET8S/oe/81/iqi28wGNjc5tvvvgnpMkIrT2UBiEs1uVkqSmJZQXkWcHe3j6hX0F7AW++dW2qPKTwXElLK4H9lUrI+fPn6fdH3L+zXmKKhOXi5Qt4funBeurEElHgkcYJtSDEVx5vX7vGs5fP4UURgzjGbzQZj8f8yq/9wbQ4h38D1TCxNWD9f/9PeHt7yO3NXR77oU/Q7R3Q3dtjPIq5c+sWpjCsrS1z5uwZTj3+fj7xM3+P7sERn/jkc/zd//YXiCohFy8+P1UeX91OnU1TPK3wpMT3SpKNs658V2SpJ7++sc58u0ajXqeXOYSSgGI0zpFKY0yBECWpyFiDcxIpNT9+Lpgqj//qv/8VlyUG65iomyo8LyCKavjVWskANwXjfhfpFJEfIn1Df/8eVZHTNoJaCjPLy5hmndq5M1QXarRmIkwmeebc7FR5vLdQwUk457AOpBAT6oQD6yZmvxMyhSod4UtvYYt4l0MLgDXvosRbizkGDVtL2BPnMN0mSTzABFDzVrFFF+s0o2yGkTzNfrfFvmvTihdINKiRh8oEstJGZQG7HUPY9yiMpjP06QuHZ6a/pJWogpIplSgoHWiEQ0uNRCIoSuKQp0sDCmvAWpxUE+NkBxQoXRJznJRoYRAUD1mW04YoDFmSMeyPqIR1Aq98sHNTWrMJJ+j1elQwZGlGqoZUoxBPOLS05MaRJglBWKEiBVYKKrUmt+wuUTA9g+4BFFPKiTWdlnhCsba4TLeXcXLlPFpWub9/i7Q4RKr8YdF+sLiJkiNbFvKJh6Y8JnGp3W6VEE0sxhjicYJAIoTlc1/+I/7tz/4s7dlZhnHG3PwM29tdkBalJUpLhoO0VL+0BUL45HlBkmaE0jvWc/rg/SidX6AeCJ59bJVeb8jO3TvgBEJqasrD5QVKCZRxBGFInqaYIieq1VhdXcWTAm+yUapW6hxDIfdfi+/p6DvADA8I/TpRtcrR7jZ+tUKj2SbNSu19IQHn8PyQOEsY9gYg4O7GAf3uEWEwvW1gJ4MktoS+Jun32br9DtrzWTt7DlNA/2iXwuZcPHuChXYN4QrQirwQHHX6vPzmLWr1OTY3d7jw+GN4viZQkoNhSi+HHz83nVzvYNBF4GOMRShFs9kgCqtUay0qjSZZkZNkKYEscIVBCceodxc72uSod8DaY0/Q8MHPD8iynDxbY2uny8xcA6mmh3C+xwxLgVKqLDyipIU7Jjtr9y6Wn3ggOkXJ3ELgrCGfCOGIidj/5MmgKEypLT1lFNZnI/wUexlkrQidWKxKke7HSBuLHLn34Ymn6MkaaVgjMgF5JSQQe2idlep2MyvcGc8yiyIfG4wIyby5Y/WhAt8n9CAKPTztoz2HyQVCeBQmw2FQk92jsCWRxhMlPlYJSxB4ZIXBOh+LwgqLHkyu13G8I53D9xRaSywGqSXD4Yj1zS1aUUg8SmhEVYQtcAiS0YhqEE6+B7KiYDAe4wURvvRwSpFJhSsK/OMID6kSSy+lQgkJQuApzWJzhtHeHq9+/VWEq3D5Q8+x27M4jpBKTj4zMTwWEol8eKITxxYKgCDwceTgBFHkUa+FSFk+d/fv3+HWjes803oe3w+YnZ3h8PAueZ6jdIAQ7iEuPs8Lhv0xybhc9OpzC3hy+mwuPnUeKSXNZrNULzzYIx+MaXqChbVlDvcHFEWKVQVBqKhGIb1Bh4yCZr2KSGKSdEivd8DFlYscxWMKZxFFQaMyPe793VFKMRlwDiEko71D/LlZcLBx7w6Xnn0W39Ns3LuHQJenaKlQWjHqp3zqfXVevtHl7u19bt3doFab3pxiZzchGcdolSMyx87WiG5vm6+9+DZnLl4m7h+xuXOfl169SqQEJss5e+lpsizl3t07eO1V3nz9Jbp7u9x45xYra2usnj7LkZHcOxgA0xXvw6MjPBVhgcIZ8sIgpYeUB6ycPIX0FEEY4kxGr7fH0cEOgRdTJB08bfFOnMI3HrLIUEHE4VFBEoa88u27PHP5xNTX4z0t3kVRlDtM6zAKbJ5PPORKjYYHjEFrTUnCAbzJfy8Z4uXO2wmBseKheU7pjjF9BEXO6b/794n6h5j5NeRA0nnsFOkLHyX41m9wbpyTpGOGQYW9+QaxP0P/J3+GsTyNpsAVFTwhEOIKB5XS19FIgcAij2EKMRh2qC/VyPIxo3Gf2dk6QnngCoQs9VLywoJ0hH6AAALPAwHGWe5tbhBWqjjhQWERnirV3cTxGFtfeukqaE3qFIHQ+NKjEkS0qg2EUOhaDWtyOt0eytMEWLq9I25lMXPNNjasAx7ywRFfCZAPWj7HENv3NUqrkggkBFpKcmf5yotfJ7ub87WvvEavV/CJzYSP/cQHGbOB1YcYaXAoQnyUkMiSWQOyfEak0scjcYWKPM3RvsYZmJmtE0alXrmnNb//e7/O3bt3ePyJy2xurmNsQWumhrMwHI4nf2dHGhf0DkcoqZBCcf7CZQ53pncHf+rKU/i+TxAEbGxskAz7GGNRwnLh0gVe6b/JzGyTUTxGyoggDChGObtb2+wpHxGPWapqHptvMrQZXqNCrd5gd+eQePh9kMkA4SxHu3cIozqe9hkNU9a3v0MiI/74lasMjeB9H/gIwq8TVEJG4zGLM02Ij9i5eZ0/+1REloz4ytWC3/3iS9QqPs++/+NTffc3b++TJglKKRCCsLWEmDnBnPRASIKwyYnFNYbGQwiDMJY3Bx5S5OiVOoPcET22SHgerJWMENzei8mtJVTTl8LG7DLVapswquFXFGIco7wqVkj6nU3ydEjoKXa2ryFUikTR3+tz9nSd2bkVFk8uUAx6HK4fcfO+ZqAkwewiz19p8MWvvcKVC5+aKo/3tnjrACMVwhkUORKDFKVGSCbLl1w4W/L7J0fdfFKESquiDLAY52GFLFlvgCv7ClPnoYUjSzS+qGC7XdTYp3WUkh4OGb7xCqMR7MxWWNtMiE84kqJC9c99Gp3kVBJLp72KcRInNL7yUBNhdk9rnJ1eVbASaRpRQOdohHKAsChdLkZ5ZssdqJI4Y0undmOxFUGcFozijFFqiRqaIstxxpJjMPaBn+f0xeqxM6dBSQ47h0iXI5zD0x5BENAdjMBZTJbgSej3B/RGCYWvOMhzDqN9vNoMRZIibAUnBcYYJjajmGPcF4srjWFxWAlYRyYNRT1gXMR0OiPyQvD2q9/hyvMXaZ+ZZeh2cdIrmaZWoBBYWTJvlRAIIXFSHssmr1aLMKHGOYjjhGq9gtJlCyYMPeYXGnz9j7/M6bUlAk8SBAFFYXAWlJakSVzqiwQBKyurXLh4ga2tHa488yxf/9LW1Hk8aAl5XnkvhoMBWZpSZAU3tzewzlGrN5Ci1KHZ3dvjxMoStUaLnYM+47hLY77K+ZV5NhOBs4Kj3X0iGeC8Y+i9OyZqdw6Tx3T216k35glDn37gM+oLMiSz7TpXX36J4SBjfu0SMyuaSy1FXSfcvnOA8wJEZrh/UL6z+72U0TEMTNLeESEp1XqLfndEraopspTCWkZYlNTkxpKLiDgZEQYl+1ULgZOC3ElEMsDTDs85AitwxmJVgKpML1tgXU7kK1rVGriM+cUmcZqxs7eFTQ5ZmGkihOXO7S5SCdIYZJrho3Bpzltvvk5n9z7z9Qqrpy/z+s0tgqIFzuPZ55+cOo/3tm1iEhw+whnAljtoQ/lgCFvqcTgLNkOgQchS1N85jJDIyf/qJDxokpT9c3ss82fjFP1P/SyHElRVI492yep17NoFmj/50xwYyV6YU/zmH5GoGBH67P/xFzl1apXevV30Mx/En18kISLVCu3KxcWaAnmMZmKzXiFSgiLwcAg8UbrS4DRSasIgQMpSR8SYgjRPMYklLTT7nT6zrRpagVWQZ2Xf/0FL9TjE2RMnTpXH/SxlNBpgnMVJQXt+lusb61jjcEVOO/IJohrnT13k7OMXuPP2Nfr7e3SHHcZpxuGRYG+2Ta1WwwlVtrWO1bMQD38earW7Ukg/bDRwQmMt7O3vcm9jnbX3fZDegUMK98CiAyM00j2gH4vvnUCOw0EWgkazhjGWWj0iqvgPqd/aU6yeXGLYjzl96gx3bl9nNBxTr1cx1uF7HqlKGfYyXKwJQ8384jJrp09z5+ZbHENksfQozTNGRY7A0R8l7HU7LLUj5LDHbK3C0myEtQHjwZC99W3OLiySG4svBIuri3zgfU9xaqbBEgG90Zi9fp9ellGvTS9bUCpUGJwtSJMxg84etsg5LBLWt/axjXnaMzWWRjPkTrK9dZeBFSxXBfiSu12LCAIiAnZHKRsHI7A+R0cxB7146jTGyRDPjRDVClk+ZlgE5HmKc5Y4NXjKQ6JwumwnGmcpkgGRH2CUZiQDiBNCz5E5S5Q7hICeNCCn99JcXlrCx9HZX2d/+xanP3qFdjMki3OGnSMG3SHjeESexuS5JUslQTbmYGePQc9ju3+HhbbPRz/4Eeonz3NYvIxlyMZGwgdOTi9d/J4W73HvNo32WaQVGFHBSI2QpvwxhlIZwCKcxUiwlLswEBgnGFEBmyOMo6CUgXUT3ZPjDGCM79P/+A8QWkPR7xLKFZqdbWSQIN/3IaSFE+k2X537XVRQY3b1BKtHO3z6I09xP4j53P/xDyhqDSqnLrD8iZ/ABhFGCpwFYY8jCFU6UM/Pz9PrD1ioLRBnKZk1+NUIJSWe8hgOB+UHhEA4GGcpvl+hWamWam1C0D/qk+A4GmZwjKEYQB42mZud5QfOPUGRZSS9HQqb40nJiZVl7t26x6DfZ2XmNDJq8swLH2F55SSH+13ur28Qeh7jOGNzv8f25jc5ffYsjYUFzl68gj5Gz1sIjTGgtXyo066kRHqaG3euUaQjqiiunHyGZ889R5QuULeXGcbrOC8hlRnaVvBcgNZ6MjATx24jCWFBQq0aUq9XadTrD4fESpX9zPm5OazJ6A46PHXhAkJ7bO1voLVHkRXcvXXI3u4RUiq+/epVtNY06nU6nc7Ueexu7fDEhbOMB10WIsWVcycRNgYr+OHnLqErdYz2eOP6PTY3dlmea0M8oCYcldDwQz/wHE+ePUc7qhB3eoRzbdBrbKUx17e2p86jyPp0jw6IggrD7i797iHDXg+t4Lu9jPedaSL9CCN8xnGB72neeOll7IVZdtZhqT1DrdJkmHT4p98aMM5ASrj+zj7/zS//S/6LX5guj8PKCgMrWR+CC+okqSV3kFrD2BeM8pxxUZAYD4tGWIv2aygrKAqLEQ7nWpCCdALpSi19k1nIpi8grcYSNr5G5kak40P+11/+RyVgwJMUGKyUOCcQJsQhSJMRqpIzTDQy1AR+xtxshSQ75NTMSS6eydjv3sMFdY52QnhsOrPs97R4v7Oxj9sGjcLhYfIUQcrifIP1booUEuEMy/OzbO13SAtbGnxKidYe4+EIZw3OGqwzD4t3kec4K/gzHzg5VR5SCGaSDs1xj6BwNOSQ1A4Z7x1yNCzo93vMtiOuPPMM1mowPlHNQ1QjwkqN2VYV7UG88waHLwfMPPYM9cVlhvgPIY/ThFYWSaka2Ki1uLGzR6vZxBro73ep1xpILTnqZIRBQKNWRcgEJR31WkA1ChFOYJ2bDNlAypzjClMNRwOsKZibmaVZr1NbOEFepGTJGE/41GsN+sMR/Timc9AnfPttFtcukqqQgzjDZQNG44QkK3jibGkRl6UZ7dl58mx66VFnKSGitoTlPdh5C+vwTU4dx+XVOs+t1hC76/SVZZgX6JkqWR4jfMAJFA8W9VJr+7j6PZ7vEfgelWqFKIoIgnAyh7F4nibQPnO1JvVGnSuXHufkXI2jUcbu0TYCS5plpEkyQewUKCWx1tDr9xDHWMyuvn6Vv/rTP8Xe5l3SUZ8Z7xLJeECWxFSrNfLCIITg4sllRBKjhWVhrk6aJUituHH7Ftk45uLpM3g4xsYQVaoIqY61vh/tr2OtJBcw6O4ThBW00AyHh1x5soQgWmeptdpUqjmH3T5Ly3PEiaVdVSSZoUg6NEPNzZ0EcFhTbtK6nelNIbqFQNi8NEFQkr5xJAYKHNaUG7wCjcgNgvJ0n7l3iV+pyR9N+f2G8kDmCsNx3pmN25ucWnEkyRFFMSQ2GVp6CFx5r01RDvKzAlNYisKgaj5CO5qtGvOySi3y2Nw94KIYopQi9EMWVyuEx3Baek+L95t398lcikKghEMmXWYqivNLT3Hzzn2ckGBy4jRje/+ItCgeSpPONOoc7O5hkBONb1MaEU3MGI4zsZRewtEb17j35jXy3phKVDDodtjY2KLbi6lKeO65p6g1fBYXllG1KqYqEVKVAvRaU1GSpuezd/sdDrd3qZ0+zcyzHyE5xulcOg+UX0pCovjOG+tINjlx4iRZasjMFo3ZNtff2eDsyRVq/piLF9pUvDpxXA5asjRDSE3hoBBglcYdU6bsgYXbwcEBw8GAVq1C4CmkVQRhBesF1OaXOBx0OezHrI7HVMI6F849wcHuDrduvIpnCuI0ptqsIZRkbm4BEKT59NAnUziELuU+pX1QgB3SORaqHtVzbX7k2fOstQqc2OCtt+4xai3Qroc4KXGeLGWCHypPltK4Trhj7rwhjAIqUYTn+QghKYqMoigXUSkEyzPLrN+5RTuKWFuco393u0TLCIeUAs+TeJ6mSFOElBN1zOMtJOPhiJde/AYffO4pDosxcyfmODzSbO3kKCWwRUFdS+7v3mWhoWjU6lRDSVhpkjpHqz1LriMGViM9RzWoQFDhW6+8QlJM/8Lsb91j8cR5sjQmTspBp1WCWr3F8mIbv95kGDuqvYJ40CEIqrQqMRbFUTdmfm2O3l6Hy8sNstyUJ0gxuc/H0NE+MjmesXhOUjhDZg2FE9gSX4QApLMUNp/Ys4GaLOKlcr4s68QDmLFzYFzZcpXT35fO4T7a7XPj+jW63R5GFkgBUgVoJVCyHJCn8biUvBWCMPQIIs3+3jaXzi4SRT7dkeKdW9s05ubIVcFse5G7d6b3OH1Pi3ecGAo5RGOx0vDR587z4cvnmNcFr167QWYsM60mAX2QHZxnS/MDJbh/73WGvQJRXyB35epmJy2Tcj43/cPYLqrcHo658OlPgdN84e/8LXQYUovqnD9zikI6qEb89ue/yPLqIqdOnKa1PMPTzz7FeDzGVRVDafFxzBOjiMlvrFMstWmcvjR1Hv/sy99lYWmBbndIklqED3Oz89zr5Ny7u0GcZqB2aM00qWUZjZU2R6MxYeCDDigcxNaR5DHKQZwVmMIcWxF2eXkZX3mTra9jHHfZ2zpg/dYttjbv0Vo4yV/46c9y9dv/guUTp3j86ffzlT/8LfLC8qEf/AhHvT02rr5Os7nCY8+9QKVWI00M+weHx/IoLAqDUhpnHU6WsqvZpF3RPl3jVB6g8h2qkaBhbmMx/NIfvszi6iJPf+QSOrAIkWGFIjMOLTTOabAgjwHiVFIyMzP7MPft3U3CMKBdbxKGEYNBj1v3b3C61WL15CrBTJPO1dcJwgApoFKtsrjUZjTeJnIBuSlRVc6WhX3aaATwO5/7PX7zt36bJy9f4i9/9sdxgxGnz5wkHfWZX1vAlwU6AKE0oa+ZqVcZJYa1s+c5e/kZxqmhPxyzs7nNN66+SW8cYz2f2fb0Pe8grOArxVFnF6yl3pxDINjevM0r17ZYbBzxzlHEQqWLrz12d3d4bEHSXjrBG9dKH8k073Nz0AKXg9ATqWODtdOfzHZjQyhiPGNwViI9ha8gNzlJbia9eQfGTGZlPDzBYSyehbIF+8AwwpYtRmfhGI4+hwc3uXntNTwlqEYRnlchyzPCiSyuQJCkKfVKiCkszkmqzRpPPn2Ry5ceo11vsrk95J0X36bznXWWVkPmZtf4tV+7xZmVM1Pn8Z4Wb1tYhMqRtmRFvv6Nb2Nu38B0txjimFtYxCUD7m1voocjasojk4KDgwMGBwe0zj9Px3M44aOzB+iEsjeu3PQPgecXLAQJtfwAqyI++/M/Q1CvEEWS7t4Nhv2Erc0jXnj+BZrNGq32LEdpF6019UYD39oSVy6gkJO5nCjxwcfp8a4uNJlbaNKoV7h7b4vd/QE7m4dUKlVWlk9xcLRJUsQobQmDGeZna/R21knSUh/a0z5xljPOE0gNmXEYo46Na97a3qZVbxEEPkpBb/+Ae++8xbh7yHyjiicdyaDH2qWnSy3rPONLX/w9ktwSVCvoap2ZxRVWFpZwMuSgMyBNMurVKp6e3lnIuu+ZPTz8oXz49WyLzu17cNjh3IkV8vGQBoIZMYQjSX9jgfb8AoYcLSa7b1GydY/jowlQqUSTU50ljcfUq6XZtJSaZPcurrPLYZrz0X/nPyIMWuhWBWcKfD9CYAgDvyRcaM13X7uJcwJPa6y02OI4K2v5OV9Lbt26yUsvv8WFtUX21m+xMN8gDMo23cps/SGuvd6eQ8aWpHD0en0GozG7e/u89MprjMYJ2g+phjXS6Q9E1FsLZFlKPBpgrMHXVZTWKCU5c2qRXj8mV2N6w4K797fwfEW12qAaVqjXq+wddtne2efsM8/yY59+gX/5rWt0OyNEAOIY6CyJJUaSiJKMpY1DlXUZy+T0bS0URbkBcI4JmA3hoKJ8kiwriVLOISdAg4dO8FPGoL+HMwYpS0eloBqiRMkrhJKL4mtVEgkBKQW9Qcwbb15n7dQaZ06f4btvfgesYqHlGBzeJ+7tcXJpEZncB1amyuM9Ld4OgVASZxXKSr799W/xthtRDw1PrCwjD3p4fsCakmgVoIQgTVKioML5tbO8Hfoc6hJB4FmBEZPfKRzBMXbeGvrVeAAAIABJREFUnme498ZrmO3bzMwtcjjsslY7xzvXNnjjlT9h894euzsd5mdPsLi4wG5tjx/8zMeImvWyz5jleNJhnEGHHkooMgq8zkZpMHtmOtyqpwXJaEigfJZnG0SBx2CQ0Wg1yxfNiwgrjrUzi9RCwcHmJqZIyI1Ba59xkpDlOcYUDAZDChsQJ+JYJqZQ7nh3dvdQSqC1Y+vaO5hxn9l6hUArnMvp765z/kOf5PDwkDRNma026Axj8jjl1PIaFe2hZLnr91RA2IjQbkJdnjL+jcI9Md0QwlFUquSL8/TTgqNOl0wWVMKI51cCbm13GNzZoXVllUyP8ayHk+7hDv64BbwwBd1uF88voWiz7RXicUySjFhN96jrHO17dMYZJxZnMdLhCygmRsOeN6DWjGjPthgMh+zt9LAFJOkxnZZwGGPxPYXnKV588Tuo7Akq2kF9ATe7QiRTfL2JnGxi/NYS3eEhr71+g/D6BlopjLUUwqHDgFqlSvfwCHGMRTWqNOl3tkAIKrUGSnko7bM4P8sfvHmXrDDEts/87CnWzi0z7sXghXzztbc4e3qFg/1dVpYilhYW+KX/+uf4yp+8zP/1z77On7w+QKnjkMkEiPLeSmcwWFzhEOVDUsJCXQGmNLyVE1kNIUBqSZGNwGQI4TMblszifpqTOzsh/E0XSdLDo+SnCOEwRY5ScnL6d5PvLBcEqQTWGHq9EcNBly984Y+49tYdNjZ7dDsFhjFLp5dpVZv84Asf5M6bb02dx3tavFdmLR0nMaYGBp791KeINGgFu8KyIyYXW0qsVUBJ2HgwuHK2QtVKJClGg0QBBUoW2HR/6jw+/zf+M26/dpXf393lQx/+AX7+F/4i+1t7/OY/+V12NrcZj2KUUiTjbfr9MefOrbDUaLB95y4V7THo9alWm4CmcBoReCRZwv6rv89osAc/NF3xHscZUnlkLit7YP0USUCn02HpRIvlhTprJ08woMtOd53DoSMofMIwIggMWkuSOEa6Aq098lRibElqOk6P99TqSZyz5KMuN958HZF0aVc0gXI0mzWEkxzee4uZxVnai6ew0uMv/uX/sGzrSMHgaIedOx0uXnma1ZVVpPYQzlJkGeYYxr924kYkROma5EwBSKQQdJXCf/wccn6Br3/nNeou48qZU3zqBy+iggV+/w/fQO4kqCWN0QVKKrI8Q0mLJzje7ltqpJZkeUa1UmVrZxOhFMGggz/bZHVhgbm5Ofo3v46t5tRmT/L8xdO8eHOT3CkWFpdJkjH9QY9Ta4vMzTW5d3OfJEuQx/Bs9H2NoGQPO6UIhOXW3U0uPXGZrcOcw2SP2ZomG2v6vT5xnNLueFTqNVrtRunDqsp2wOrCEg/YyGJV4Mnp74uSinG3S1Rt4EzZjlACbr99FT9ssHX3DkszISsnV+js7lFrOW7sbSIXl7lxa5uZxRWGoxFf/hev0GiEfPDyZX70h/8m//wrL/G//fq3p85D5ClLKkBZ8JSkl8UoBKGUxAr6cYJIMwrrEA/62bZkeBrrGI4L/FxRhCl/5UTIK1vbvDh2aBeW0NYpwxZ9lKfxQh/nCorCAAbjwDxwVzIWU1jGWUGcGXxP4UvNvRv7rN/eI6i3qLbOcPL8z3H+Qpth/BJf+oPf5dzZ2anzeE+L92I4Jh4WJAacDck9j8xZpBXEvo91YEVJ7/bsZGg1WVGNsGAN2ggKpcmsRQuLxJAnPUbDw6nzGK6/zWDUI3eCUW7JTco4jSmMIcsNvu9TrVax1jIYDHj9te+y94tbnD1/hsX5WV579VUQIfHYkGcFSIdTik/8yCmW29PjNLPUoJqSOEnIi4SZuQabm/fwoxJXPDfbJIm7dMZHNJo1XJGjdFgWaKUpXA5KEOiwpF/X6wzz44tWhH6I8uDu+k3Wb95gpRHhCYGvNeDww5AszSn6ByRBRGvpLP78EnGa0O/3kC5nplFFAbVKFaE0WIPR+lgehfZdpydrLcIyYUuCcwVOCfKZKt6ZMxzt7PNmN2FJahZrPhcfm+drr7xKcGqelSdP4rQrP+smv/cYeThn8bQuh09KPqTYS6XJnSYII3ytCWpNhFI4m+NFbTxvF+UHJKME3w/ReoxUCmsgy3LEMXZ3AIuLiwx7R5giKzHfWLYP9tl78Rt86MM/yPlzZ+gdbrG7t89oOCbLDUGlhucrhLPUq5WSlQxEQekWb4xhOBqSHAMFlKZjlKexJqfICzztkxeG5sIpkuRrGBVQDRQb9+4x06yihOKwm6CH97mwdJJvffc+Z9Zm2D844u27+yT5d2kcrPMTH3s/Lzw7/YxIYEmKBFsUFBKkk0inEMYxzAfIqIGUmuyww0POgLMoZ9BYlmuOmsuoewUH++v0ugY/r2OUPd6Q36mSWyIkSno4V7LHLZLCOvLJO2iLcibnaVV674oShipsTjrsUnDAuLfHxv2Ee/c3Odp9k8bsE1On8d4W7xr084RekpLLiEz6OARITWBKbO+D57vwc4ww5UWiFAkKbYFQglz7CECZDFtkeDLF86a/+MqHD/zQh/n2t14nTguMkyjPBwl/6bN/gfu37/Hyy6883L22W212No7oHQ2IIp+9gy0goN8pCIII7QmOugk/9ZNXePKJ6fpVAIedATMLLSq1JkoqcBZnM5LE4KRic///Ye9NYy3LrjrP3977zHd+8xAv5siIzIycR9uJbQwmgTLF4DZUQTc0tGgo0S2kklpVanULFRLdlihVVwlULnXRVFOuobGNaQONwTakjbEzk3Q65yEyxhcv3jzc8dwz7r37w3kZpvtD6z6plA1SrE9vuPe+9c7ZZ+211/qv/38HaQ3TS1N4SqKkZDjKCMIAbSBLU1qNBp6QlLlhPE5Ik8Op0yOczgPfZzw+YO3GVXxHoDC4UiEsKCHxPB8v0uhkSNrdpqi3sZ1lUJo0TZhu1slmpimyDNercOcYcB2JPmLZpOKrq0omge+R65KiLBEKhFWkLkTHT9KYOY4cx+xqj6K3T9QWTK0rXnrudaaPT+H4LhJTDfDYikdnUhOiIv9STlVH9vyAoiywfkTDUVjhof0WTlki3TpG+GS2YpYTvkcySrDWUq816PdHFLmhKCpY31FOAO12m1YjRAko84zBaIzRVSb+9qVL+L5HFMD+3t6hpiuMkzFiKAkCn1qtVqm1C0FeVAyJJYdUEkegLciSERYqsitdMSwaq2m0Z5iZalOqEBn5HJtzcZTPy69cR480pdWsFjso32Fzu0+RDPgPn/0THnvgHO9/8iGCxhXmVu6a2A+DYKATHGswWYXdLpD4rsuMMLiqpL0UcnxxnsiXdJo+C03FQj2gE7osN0ICxyDykn/0v32efqloOR4jW1KW6cR+WHsoHGzA8TzyPKcoKvSLsQIhBa7jom1ZvUYIikNtz1IKrHGwukCPVrn0zudp7y0ThXWajTlOzE1O1PWeBu+XLm9APSLXPtq4aK/AWoMsMtThcaMirjIIIxDWRRuNAAqryFWOyXeRaUIULpMDwuTUg5LmEWp4w/6IE+d8PvTUIywtr3Dz8gGhH/HIoxdZXbvCW5evk5kxM7MzrK/t0evHSFES1jy+7+mPcr/3KKurt3jhuRdpdyTHT05z5vws7emE3ujmxH7IKOQgzvF8g1IShWZ+ZZpxltNoKZrNJnle0Bvt04hCap5PLfIwpkDnFptnbN3qo5RX8YJ4LZQnDpkFJg+aYS3i1W98iaV2jbuX7kOPUowxeJ6H4zjUApeidLAGPD1me+1NpHVxlMJoTWfhFMZtEhc5roA0z6osGUFRTt6Q8t2Iml/SmGlRr9WZnve4fn2L/f0c6UlKqUApepEkzTL29w5oLt3NZq1kXziMFkfMHOSYl95Af/cDpK5AIvEJEOII62M4IAirtaiVqQjEahHClgyVYckaSq0IO3PsjQt0kVIYi5EOvvKRUhDHI4zV+J5PPExxlIMoiv9PMen/t338xz+BsRnjNEEVlrnlKjEYDYZ8/jOf47kXXuDEyhQLUx068/OUVjDY7ZKmGWEYEscJUqQ4nofrB7i+h+t7BFGI1pOvj8Fg/xBeR7VBOQ7CCIJah7sfuJ+HhOHGpdfZ2epSxn12NjcJa/NI5aBcy3/+gfv4+stv8+K3tzB5zl987du8/K1X+eEf+37uvXefsw99/0R+HJOGB9QBf/d9Z3jfXcsEtZBRf0SelVwbVeWJuTBivT8i1YI4N5SJw0Yv5qXdPv/X87eI0UidkToBrhV4RY622W2qjUms3Zph0LtJkRlcpzqVaaMx0qfR7uD7Aa7r4kqXZi2kHnoMewOMsNRaTTJpGcVDRr0++cEuO71bBKHP09//MVQ8efnmPQ3e04vLXN9YJxXjiiFQK4Q0GCSFthhbIqxF2QhrBRZDaTUVul6icksZ7yHS64yHexhZo8x6BB2Nc4QM79jiMmHY4vRdpxhnGXsbI8KgpN3s4MiQV1+9xNzSDHfddZabqxu4XoApNWmac+nSJZK8T6tV58Pf+xCPP3aOZlMR1KFZi5j80kN7fpob19eYnmnSakeM4wHC91iYmaXRckEk+DWXSAWEQUCZFoRuiNEWR0KuCwLfxQ1q9IcDknifUR4cYlonN+MElAZcC45QRI3G7WnC8XiMLkoaYY2sMKRFzijNcA626A1ipHJRzYvMtea4fOUt/uBzf8DW7g7BTBuv3SQexXz8R35oIj+WOstcPHucaFqQmpjrm28yiodAQIkBYZAodAH94Yjp5QWu9zYIawF5AOHJBWZCj3dev8RTg5ys7pG5FitMRaw9oQmhqhKHELeZMMuyQEpJ4jfYSHKW4i6mFJSDIWsjxdmFOmWRokU1kCOkIM9ywKJLg+t5yCxDHWGBdJOY5nREe2kWlcDG2q1q7sFRXHz4Yb705S/h9yQfed93UZub5frmJvfNn2B18ya+Xz1DQjkYbdF5QVmWCCFoNpuH2eNkphx1OKSkscYQeC55PK4+L5wCPcSUBVs7PXQ2YpRZ7n9ghRs3tgik4a9eu8TrlzbBloSRQ/egxPEcvvnNbyGl5O9M6MfoYIiMMm7cWMOOS97aK9jci8kyzfX9XTKrubAwR5GVCM9DK4epuWmagcP04gKBvEaZ57h5QXDYS6Gw6EOGxEnt9Ill3hjtkJeGPC9x0UjHRToKz5e023Wa7Rm6fUPU7DA7U+P0WcE4ybAoSn+aQZyTxwU2kxS2y17vJo88dj+R8zc0eNt8j5XpNonrcWWrS5E0wckwUuHnPplMKVVJZJ2q4WVTtDBo4yEkBLkHxQi4geAAT82izRApfFxvdmI/nv7+h7m1L9jZusVgmHLlnVf4yZ/8KYoypl6fYWZmHm1S8rQ66hZljhKGWt3HcVJ+5IceYX6hQ7PWIBnv4rklflBHSYk9QkbjCMv8wgyuL0nzjCCMmJ6ZJopClM1wPBctIXA90AZHCiI/IIlj8nGC0QV+VCPXJVlRMIhTMnt0EtSBhmGWY8mJY0tSlHi+T7vdJopCtC5xPYc8KSqelbLgxpuvEKcF5+6+n+E4oz3VYTRK+KOv/Bm5MDiNCBmGRyoTPPHgY3zX/Y8z0re41b/G9fXXqgk4DEYItDBIKykSS63WotFskucJVmg0AuEqWivzzGNYf/0dFh44TyodjCrxjoDjdQMXpKAoiop2R0kwJXmRY2TEnlVsr26w0Knz8NlTULNYWxKGPsMsRduq3CeFPMzCPKTIK+IyPXmtube5Q28nR7WayNLBFgWe51GMx/hRjZmFRVY311lePselm6u89PLb/Oj3fpQFlojHo9ubr+u6FWWtUlVt1hiKYnI/pFCYwxqyMYZxv4uQkjxJKbt7iMDB8V3eubKOUpa56Tq+Y2lFsL21Ty+TjMcaWQrWewVxbmk1Sga9fZ795jcmvx665K/2I17ayzDcYKBLMAZVCrLSQSrF7tvbCD3AESFSRIRrYzpTPrOtFrPzi4R5DElCP05JtMEpSxyR4xyhYbkw32F3b57BcEg9qpEOx2TWxQjFsZUlFpcWaDQ7bO5ZZhodIr8gigyzS0toLbm2njDVmSWLCuK4xJczeO3jCDXD1PzkhGHvafBW1mF/v09MTt1rkSkfqSwSQxmAUy7iaIe7T1zj1lbIKG2T27iiW5UuUmsaUQNPLTDu7RCqNkIYrl3fQprJ2cnmpwyzx3xef/sWW1urzM4ofv/zv0WWpfQHOf2BxALLx1v8zM//CGfuWuLUyXlcWaLEAL+2D0hsmeGYFlJajClxgoysmHwRpMmQdrtRde9VwBMX72N1dw18w+bOgKlWC9+RTActsmRMWab0kgNa9QZ+vc5m1zAsStAWB8nK7BzdwZGRgnzq330aOdqhpQwnZjssC4HJS3Z2dnDRlDaHsYMI5hkkJcJk5P0+RdjiT599lh9qzNGKPB64+x4e+MhTrB/skZUljnCPNEE31QqZDhos+I8QuDO8WnuZcbZNVmQoW3X2vURwcvoCGwfrDEY9ggUXdIkaAy6MHIt3co72jKVfZkihsCanLCbPvEsLrh9gyXCDACsE9XYLKSEIInSeI6KQ6/0Rm6+8zUOnT+DVaniOQz4YI6WD4wqMhSIVFTbeGjzPRZRHQAE1Z4h8SGoBuXQoclttBEqiS80PfGyRP/r85/jX/9M/5+zdZ3nwxDH+/CtfJM5STp48RbPZQCpFkiRorQnDkDAM6Xa7+P7kZSTHcdF5dsieWGCsoMhzQl/xoU/8IjdefZaZs+/nay/c4n0PLDPsHzDVcnj95X32DlLWR4p+P8UYjRIVtrkZugx6Be3GcGI/6mlJL88P5yskofRwixyvKKkJfdggNNT0NNqUlGQkowOSkWG7uMaDD56iNjbsdoeoMYR+SBmFNLwGTjb5mL7fCjh/4QytKOBgfQ3p3Utc1hmmOfc9tEhW7jMe3+IjT72PZBzjOS6e1yAI6ty4vs6xuYLpWc0wg52BxpY+4/EUX/nqa/Svv8B/96v/aLL7MrHH/wkspUU31xDUKY3AlRLHsfiyoK8jfCclclPqqsSXkEsO4V6qYgszPRwymn5Ed3iA0nWSQcJ4OGZp8fTEfig5RPm7PPhoyIOPPYSQLkIodGkYxS5b25KiKDh3YR4/EFghMFmJLQ3mUO3GGCiNZjyuhjKU9NAlSCafXCuFZdDv04jqCAT7/QPSfEyapmBKTJZjC4VxHOr1OkLUyMYJURigtaWfpCR5Ve5RxsVxHKQwRx7SKeIRQjr0reXqfsxukaCLjFroMd9poOMertacuXiGMhujbEbc73PjYEwaTvHO6k2C0KUuJR98+DH+6rXXWN3eJCsPp9gmtN5wl/WN66wsP0LkzHN8+QxbvX2iwGDLauw/68XglXTCJuN4gJUajUIYKKTFKgBDdGaZZP+ArMhYmpohH0zOXuf7PlI5uL6t6raOwvN8tC4JgpBCCrIsQ0lJYQyvXLvOB85foO2HbDHA80MockqjQWgcp0Ik1KIaeT55kCiaHjYK8UUNqyySDOeQJrYUmla9zlOPPcmlP/wyxc4Av95kym8S1juUGobjMVE9ojPTYXQwAgtZljIzM0V5BFSS0RU9cVFkh3S/GmssjVYDr3eVdO0FsqnztOqCvUFB5Df48+e3CepT1IHtaztIASWGhbZLs6GI44zverCJewQebT9JcHVZZf1SEaBx0xyvNBhPYJRCS4cDq5BK4QqNM84QGjzr8trzlyl1jicVykpkktBIM0rXozwChHNqrs3y3Az5eIQphgwGMeN0wGAYs3VrjHRyPNfj1o3L5HmG4wpOnTnP8ROLnDt3jtUbN6nV6/TiMbde+BbTnTlqkWWq0WFGnZjYj/c0eB9kIdHcabQKiNAI4xEFBW1fI/Y9HGcXZW4yHjWRJqJRs4z2h9TDNlIa8nyHdLDN3sE2tsjo7myBDAmUQ+BOrgwyHnk06y5SGZAZruMihINRLrYIOXNiBumkNJo7CBIsHpoFMivQOiIeaXyvQJshwqvISJXrkqbTSHkEXmBjqIVVWUJg2epuYEQJlATCkA56+PU647ggCqfJ8ozAU/T7++RFhYeemZtBCodM58RlhnG8o07HI22B4/pgLbG1jNGUgJtLerEmKH2mHBevOUX/Zg83T2k3pqllGuM4vHPzGuN8yEJUp5SSstC4wsE48khlk3oQcG31KsuLD5CPEs7O3cXG9S1aS3Nsbu5Q2pI337rCiC4DN8ZpV/zpVoBQh5gkA1ZaEpEhQ0lQ+JxcWqHvTw4lVW51BHcdBykqUjSLqGrHxiCUg+PaQ1UlS1IWrO7tf0fGT1aCEGEYkY0GFae0cnBcp6qzTmiB7+KIquWKMFi/0jN1XZcsy/A8j3sffhh5MCTe3uXWzj6NU4tkZY7j+vhBtfHkeY4QstJCdagw6N3JM15tClzfByWQRY5NEoo8x4o6SRLTaLW4+fZzvP+pD5KVGTdXbzK3NM87V26yN4yqiUZhiQKXRijJxiXtQDDTqXEwmBzl4Zvq+ispcQ55kIQA4QiwEmFVxemtLAqNSjLqSUKhKk5vjcV1o0obN8tp+JaZFY+t3phkf/KTyO7BDqfnlvE7TZz6Ser7B3i9GLXZJ3Q1Srl0WjO4QrFwbJlSaywl+/s7tFuGEyfPYayl1cnZXLvJeJwRNUqm2rMQnpvYj/c0ePvWI84NxgxRUlJSUNhVdH6T49EU/d4uni947cZNsswh8GoEQRNfOQiTkY7+GDcNWGqe5KpRrK7eokg1jz30OPcsT46vjuMYvScwIieq+fg1TZaNQARYVeK4XRwXdO6BbWBzUO4qgWvIqTM+AFXXBIGHcAyu55PZnFyDOkLL8u9+/Bd55plnEFKigTitOIWtsShHgWPZSww61qzvW4SoIYxFyui2gGrcreqRmfWxSiGCAOk6qCOM6WskqgThuJTCIh2JdGsYJGPlM/ag5wj2377Mk3df4GS7xbIBtbPFq9duUGSaq1ubXEZTaoOxYF2FzY82ln7P6btpOB2sTGg3XOrmJMc+fIJ6NM1nvvgZSpFTHIeTzdPczNfZ9veAQ91PWY3EC21wBGRxF2kcZtoL7G7vsr6zdgRPBMpx8T0Pow15qbFZjgSysqBZb6BdTak1RhsKa7m0uY3B4B1K06VpThSGFFrTHw4otcHTmvIIteaZ0lBzBIPpAM8RkGo83ycMQ7IsoyxLnDDk4R/9GLYscR1BEDhcufIOb7z+GseDGcpRQhKnSDdAG4lCUavVaB1BfMBx3ENptwrLa4sx9XaLWq1G952X2Lj8Gtdee4c//calCpXkCFTnNF6twebmO4SBhzUlNV+w0DAM+oLl5ZCvv7LDIJ58jSijcT2HwmicwuDlJaLIUdbQqdcYDnvkRcGiZyiyEl2WtGanGFkNrmIwskhTIAvJvU/0kGWdeC1l5USNsT/5SeT999zLMy98k7zXZ2ZpkVPHT3H/gzPs7e3x5htvcOLMGZSSDAYDtjcKinKM2vVwznlIGdBsLCOlodvb5+yZM7Q7U/i+x/7+Ps9+40Xe99Qjk92XiT3+T2CByNFWIxyBo0KsM8bqjDzvY8Yb1Jw5iqLDINkjHeX0ii2Wj59F2BrWjDDFgOXZZdrUKEYlzUYLVZfMNWqcPzYzsR9GxNRq01jh4rgK5QhqToh0Qnp9TdLzcVoeuRcjZaUOTSkwQpCmOX4U4Yb6kAEvp7AGKwIsHKJjJrP77n+MdqfSzRMVV+Jf+7oiird/vYB9yMdw+1uolCmEC0JX7xeChYWFSg5tQptenEVpQ14USE9VohJKIoQkTXNsaXCtAlexvT/AjEvGOmMviatN5NApRzooaym1piz0kTDeAFub29SPTVPkOUIa+v0hUdggzwvm5+f5q1ef48bqGp2VWVYurNA9GFKgb+uaWlNJr3VaDUwxwuSWYCbi8ttvUsojNAp3h8zMTFOWVQlKKQVWVNORVlBqQzpO0bpqSgok0gFrKnERTylcxz3EdkuMsRVRUS0izSbPNGt+iOt4VQMVS388IE0SyjRDG8NgNAIN3X6XIk0Yj4ZsH2zTHfUIXYXptJBa4wYBUaOB40hGowGD/gD3CCcAKavM1ldV36kYrZHGu5ThRUZJhmxMMX9smdPHNtneS9jcN+yMNjh3epmiKIiCgN39AbVQ0M8ctKu5fGOMV3c5fqw1uR9Zii4qlSeBQpUFoihxbc6xuo8OPdIMZgNFlsL27i7HWoLcSLwoYFPmdEcZg0SzODPD3uaY8TBhbqVFnPYm9iMQihPzy3StpN/vkyZ9yiwCrUnG40q+0fPx/BBrBdKBIArIs4w4HmBsQTwc0et1aTXncFRAENRZXmrw1FOTl13FUbmO79gdu2N37I79/29Hm9e9Y3fsjt2xO/Y3wu4E7zt2x+7YHftbaHeC9x27Y3fsjv0ttDvB+47dsTt2x/4W2nuKNvmVX/0x+y5V47sySPCuinql9v2uGrxEgQXHURWZvjaHr61QGEYajJEIoUDkWAv/5H/8PydqoT/5yIN2f2+H7378AU7OtdkcKeJhl3vPrnB5o49iyF3HmjjSwRqXZnuaRnOKQsMgzrm1vU6eF8RpSVwIokAy1Zlhv58jpeRf/Mv/ZSI//snfe5/1GiHSUdXgQ6nxHBdlLa6q1EKsMeRpReJfFAUxGmSlIm9LjfMuHllWaJUiLxgOR2S55je+dHkiPz75o/dZzw8IggCkYjSOEUJQq9XJM42xGWFY6TFiJY4XMBinOBJC30cW1bj1WB9Sax7qR3p+QFqW/A+feW4iP/71p/+ZbbVn6XcPmJ9bZOXUWQJpSYZ7rK3d5Opbr3DrxhVCX+IHDWqtNvOLx3C9kF6/h8KSDbbwfZ/O3Dz11gy9gx3W166TZxn/+Fd/dyI/3nzzTWutvS2DZowhz3PyvLq/7+K5y7JkMBjQ6XRu/9xaWw1LSXmbi75er1eahllGURQ89thjE/nxbz7/gm03PLJSU5QajGV3+xarN6+bzq9BAAAgAElEQVRx9z1P4ro1tDU06j6CklrN42DjErMLS0zPreC7lrqK6W+9ymd+/09ZPvkoFx78MKM8w1PwsQ/dP5Efn/oX/9SO0xJpclCWyHNYWj5Ga3oGoRM2t7ew2nDh3FnSomBjc48H7r+Hm6s38L2QwWhIq9PCUS7X3rnC0uI82BLfi1g5fpKFcw9O5Mcz3/wtm2c5ru+Ro5FOgs3Bkw3GA8XLL73KaJRw/8UHcZVLGEQ4jsfOwS7WhYXpWfIkwZEutpAMB0P29/eotVoIIfgvfvbnJ/Ljm8++aE+dO0/oCnSR0+v3mJ6eRgpw3CqkVmLV7u21UhT2O1xxouJEt1jiJMcg0EaDMRxsb/LIYw9P5Md7GryFEIeUr/ZQdlLy7n+kD4MzQiABJSohT8epRqyLopIvklKhyxJtCyoNUXvI9zy5H1GjQcuHTuTg+RE3X/k2jzxwF4vz7WoYoigQpsSNOuSpIC9dUu1T5gW+G3H6+Cl0nmKVYmdvyOLSFNOdaZ6/tIs1k0PBIk/iuQLlKRAOpihwhMJzKxpWpSpCriwtSNOMJE1QbkWYpMuSbJygpERKgedWAwraqRR8fW9y/KzvRYAgSTIMliRNDgMPKOkQpykqaOFGHYZxSp4KhmWAIzQNx6PuCEZxjhEQBP7tQJWX5VFI9OhMz7Fz9TWEEyDEIo5yKLMxRW5JRgc4vsvJsxdod2oVdC2sURYaY3KarSmKIuVgvct4ZLCiIoca9Xfp7qyRMfkQl9b6Nh3wuwFZCIHjVI/Luwgta+3tB9R13f8H5auUkjAMSZLk9mdorY80YZlohellCEqsdNDWIWwtc98jx0j7e/i+BOFQCwyOcDBFybPPfIaZpTM89v6n8T3YvfYtLr38l3j1BT76PR8kai1xc2ePWm1ySNrc4hw313Z57OGHaM908KXH9PQ0fi3gP/7Ob9GZm+XU6dMM+j1WTh5ndnGFre11nKjO9Owxjtc8Sp2zu73LKDV05o8T1kLWrl/j+pUbLJx7cCI/orCJ6xTVdS10pZrjCvLE4ijB+fP34jo+UVAnSzKyLOOVV19GeA7Lp0+QlwVJntFphFy9co0kTQjDkF6vT70+Oe5dm5yizAikRJQJf/jZ/4NP/MSPM7O4VCltCXF7464SVYGU7yaqBjAIVXEQBaHCoEjTktJoyiMQqL2nwdsVzmHQ1RjkuxT3VdZoDnHKFsAl8BrUoyblYXZZqIyy0LQ7Hfr9Ht3xXjWmrgHrYZj8oag36hyvN1meX6A1PYMUhhMrC9Xfzw5wsDSbCyRpjuf4zC8d48IDT5JkOVG9weaN61g0Vln2/uJZNm5tc+WdVWbu+W5svDqxH54jcQW4SuC4Lsr38BwH13cOB20clONSFIbhMEaMHfQhFtxoja8UHAYW31UoKdFaH+KkJ9/NRqMYPwhxXQfpCNyyIM0yHEcRRT5G1uj1MwpHUeATFwLj10kHB/QLy/GpBoNiiCNyknxYZRtaU2vU4QjDQr6jMOmQpOzS780h9BnS0T7j0T7xaEQtajA9M4OjFONxzM7aFYJ6i7wsaLVnSbOMoiyxJqHX28ON6iAUu92MneHk4/HvBmspJY7jkB0KPgoh0Frf/r0QAt/38Tzvdpb+btb97uulrKgWqiGXSlB7UhMKMiNxbEW9UA9dhM65cel5Pvvvf4ephUWm5o/z3/zSLzM91UTYHD9wyNN91t76Kvs7m2yvrxIPRvzcP/wlZhYW2dodMohTdvZjHj43GXf0+z/wKDf+w+codEy3rzm1eAxjM4TyOXv6ODmSRqNBMR4zGsZMtTpEjod0I/ywwfRUwM31Nd54+22++0PfzdLxYwgBke+wfu3GxNfD9xoIChzl42hNkRb4TkCt4SGBVpRTlpbAr9E1XYqiYH5+lun5OVqz05i0oNNoUQtCxksj0sMTbVpY1BHG9LWtJO0c4VLEKS89/zy7/V3+s5/4Sc6duQtjORyik7crCVUuIA7nIhRSSKw1eE6lci99n0y5RxJCfm/V44sYoy1SKnypaEQNoijC930c61EUVdbaaS0xO7WM7x0ONfy17NoYi1yybHZ3MVajdUE87tMbbE/sxwPnz7DS9kCXfPMbf8ZT77+HzVs3GI1jOq1ZppbPsHD8Am+/+jzJOCWqtQinZhlsb5DrkqDeYZzErK1e5YkPfIgXXnwJle+xd2uTc2cm5yaYWZjFVRXlqOu6jMdjNjfWmV9YYqkzj+97+L5PvVYDLHlecOntaxx0u9RqdWZOdCh1jrGaJBlXqikWZmc6R5J1cqIQ1/Uo8pxed8Dc3By1WguBIcsLepnPW2sJL1y7TBD6tNtTnL/7XtypU3z70hu8fPkGD55c5kTkMTaGtEgZ5TH9YYrOxxP7UQw32du6xrn7HuPxR54gz3O6+7fAKJqtJoEfkqdD4tww3L0JQrC3cbUSCkhjsiQlHg6xZYK2gu3VaxSF4LVrPRZPTH5ftNa0WtUEobWWbrd7qKdZlYeSJKnWrOOQJMnt97ybbQdBQFmWlGV5yKsd84UvfIFf//Vf5+TJk7z11mQ6hd/+o9+gt79LLYhwbAnxKsNb6yjh0iw0/cvX2HnrWX5j/essLS8QBh4b3RQBvPb8C3i1GsNxwtTMDI3pB9jYT2k0Qh6divDdydeH59X5sR/9OBuba8zOzBL3d7l+aYOZ2VkcFxCS1197iRPHjyFkgVVjtnfWWDx2ijTZ5Y9+7zkKPJ544v24DcPqrbeJ45havYVoTp7xdneHJOMEx3VQbsHmrT1ajRaeK9i60SdNCp555mv0hiULy8e4+MBZnnjfBZACnY+Z63RIxwnxcI+l5Q5+EFRlyVIcjUb5UN2rKEv+7KvP8NxLr5K99DK//enP8l///C/wD/7BLxIEAXlexTNjzO0cRkqJq9yKrMxYytJQGotGEic5eTn5MNl7SwkrQBHQDNssTa9wfP4Enu9X/xA5Wuc4joPvtdFGHZI/Vdmk5fDIYStuidMr54njIUWZ0VMe7hGIkhuRj2syrq6vs7vX52RRUpbgqBAjXLwgQkpDlqWUOVx6/RKlDMgLzdzcNC9/6zWKbESeJ0SeD7agFQl204SbO5OzGzabTWqhf1v523EcNjY2uXrlOqNRRqPRpF6vVyRJrkuSJHz75dfpdvc5dWqFs3edQAiLwBLHMXlR3j6al0egps3yDE85hEFYkT9lVW3XdRRZDuu7PW7u9WhNzRBFAcYYgsDnzLm7GCZDXv7LK3SnmnRKTS9N8eoNdntjtKMJvcmXmC0SjLWcuutRHC8gHg0YjfYp05y0FEjlU2hDbsAiESYlzyuJsCS5iZABZVlQFhqZ52RpSrdXUJaa8Wgw+fXIKga9v16zfpde9dlnn2V1dZVWq8WFCxdYWVnBcap+zbtZd/Wwyttf53nO5cuXcV2X/f3JOVZe//ofIsoEB0nkOizN11k6PsvswjynjcPa+hprt3Y4vtCh06kR1gIGac6NtR16/YSLK8eIfJ80TRgM99DCZTBKmGrWCTxNe8Lhxn6vx9bWDmtra4xLwemTJ+je2OT1b/wVS8uzLKycYmPngCBokeea9fUeyyfvoTPVYmPjBjsHfT74oadpt+cQvkMrsswdC/AceSTh38CxaKVRwuI78M7rb3DvPfcytTLHc889z3icsr6xQZIE7OxdIS9HPPGB02A0tjTEsabIC7QpMLlllBjCMGScpIclysnMdVzq9TqDg23euXyZ/jglK0rCep1/+s/+GfOLS/z9v/cTVJl2NaErJbfXky4rXnRjLdpYSgO5LslLc6Ty73savCkFd524yHRjFg8fn5C6W8Na0GlF2yk9iasEmII8S8jjIY7jYFEY8kPVCkUtmCf0KoIeB1XJiE1otVrI6luv8to7t2g1pzDaoTM1zfbmLvt7PUajbzN1a5XhYIAuPYajdbIyJ6hPc/XK2+ze3DnMMCR5XnAw2OPsyixm/iHcxuSZRBRFdNrNSqrq8Ptms8lrr77F3u4+m5vbFKWmtFWdu98fksY5risIowjXFbiOwpSG0vOR0kEbjVKKOMkm9iPLMnRWEvgefhiSHgavKhuXGF/gtzSd5hSLCwsYa3nttddotjosLy/yZhSxubfH7IwDCNKsYL+foELJaDR5D+Bgb4eoWSPpb7OtDVZJsqRSSgnay+g8pxhnzC2eYswIR0K7U2dza5PtrR2kMozjmDLPUaGPzjK2D1Kk1UdSSknTFHEowvBueaQoits/G41G+L7PM888w6OPPsrjjz9+W+gAODwq29vv6/f7uK7L8ePHjxS8hdXUwhBhJYHv4gUec8fnWFhZJhkbXN8QhS7zC7M0Wi2QcHwBtne67BZjhoOEdqNOHcH11beZWbyH4TBnv5fgCs2ZY5OJ3R50dxiPB+zubZJqaM8uceyui0gv4MXn/pJ7ZZP21BKvv36ZixfuZW1jjQv3LvPmW9fY3L7Jh5/+YYKgxjgtUFR9LJELdKHROiPyJ9tFHAVSWBxHoIRlZ3ufwFtjfn6JW+vrDAajqg+GYZwM8YNFlJMjhQPGATTIAs8TlGUB0mIokKpEHUFGURwqVV25fIXX33idD33ke/jaV79OWWrSNOOzn/0sT3/fR5mf+Q7fkhUWecj3qY1BH/ZBjJBVX0hI/KDifp/U3tPg/eT570EWEifR7O2vUlteYFz22dg9YLS9hbE5tUadmdlTdKZq+NJyZe0aMzOzOEFIlueHCAzL+juvEbgeAkmcxtScyXfwB554nN+/cZ13tt/hxx96hMGgS2//AF3kDGPo7W9y8b5z9Pp1+nGC1j1efu0tmpHL3OISOHWUyjA4XLu5g9eapXe95IP/5UOUve6Rrsm7tVClFK12Gz9JWF6ew/Ek3f6IpDcgNRLH9SmkQHghx08tcfLMaVwXbJEjtEXY6rNKXVKv1wmOQDwkpaQWRigp8X0fc6iiMxyPiRsnaLTrnGwdVE2qIOLNN9/EUZLR8ICH3/cEJ078t/zbT32KrdLjwTmP/WHGUtOnFrj0hpP3Ika9LWzp8JU//H0e+sgPo5J14m4X6QVoHMJ6ixPnn+Iv/uMnmT9zjjfWujz91GOcOHWebm+MUXWarWlcx+Of/8//Pf0UCmM5ftc52rNzR7onn/70p9nd3aUoCkajEfPz8zz77LPs7OyQJAlJknD//ffj+z6/+7u/yyc/+cmqfpqmtxueQgjG4zFf/vKXuXnz5m0yqUmt0ahzaqFV6R8WOY12A0f5mFJRbzcZxEPOnDnJ0oklhPDIsoTVtS0c5TE900FJjbU51lHs74xZPqFYWp5hMEjwjxAkwDA71eDURz+MlJbBeJc4ybn33vN88MMf4I0XX+Jz//7foTyXP/nSH/OxH/pRkJaVkyc4d/EirVoToSTGGtzDBrtAgFEYM3nS1R/s43k+ZZlRlGCl5Pe/8Ke8+O2r3NrcqRruBkoN84tTvO8DF1FOgZCGwFcop8ClQKDIUh952JMQrkY5R2B7dBR5mnDy9Cl+8zc/xdWrV3njjVfZ2zvg0Yceprd/wM/89E/zp1/+k2rTpwrYEpAW4rwSxIhqNWRZ4AGeFVirGLiTh+T3NHg3ag36WwdIR9A76DLdaLHf2+GFl99ia+sWSlpqYcTJlT4PPXQXYaDwXA9jNLrIMKXG90OEI9nf3iEbjxBW008GNFudif2o1Vo0Z1bYH8bs9fosTjfYXF/DlJrW1AJZMiQMQnTXBfEdyJ6gIMkykNO4JqMzPUt/VBCLDjJYZvPaOh9+cnFiP6J6naheNbCklDSaTTzfp1FrsLGxTxS1yUqP/uY2SZmSjcfIoMZgNCDLcnzlU2gojUE6qpIxc1y0tmTZEWS/EJSlxgrLOKmQJkII0rxAtaaIVAPfcag1auRlVRKYW1jg3ov3cP78OQajMc2ZOa5t3eTxxTmm6wEKQc2VzNYny+4ABv0uppSMSkWrPc2t9W9jhF+p2fghy+efZLizxtW332Gz2+fx7/04G5ubeE7J6bMXcOvLDPe2+cL/+kk2eil+WKMzP8PyqbOER2gUDgYDVlZWmJ2d5eDggCzLODg44NFHH2V9fZ29vT329/e5ePEi09PTDAYDtNbU6/XbZZx3ESZSSq5evcrOzg7D4ZAwnBz10h0XXAg9dF4glUdpKiHheJzSDCJqUZ1+96DiuxeKzFjK0pIXloPuAUWeMT/XodFuEngO8Sgmb/nUQp/+YPJehDWQFhlCarI0wUgPY6A/6CMljHoHNMOA3X6fsjQc7O3x6ksv4UY+jz/5vipgSgfH83Cke1vFRznqSKpPZVnJygEI5fDkBx5ma2ePy1cv0e32SJIUR/msrJykVg8otcEal1IXGBEjtSBNY4KgRpaV+H6AtIfIpCMomGRZhjGGqc4UnuMQBAFSCiLf4aknH2Vubo6r169zsHtAZ6pzSChXQQWFFHzuc7/HjRur/NzP/SzTM1OH16ASVP4bizYxeYYjLcloTDIa88orb3H5+i1WN3v00hG+62KLETu7GUnS554Lpwijw9qjKSnylMD38bwAaywHe/uErmRxsYNlcj7eehjSajQIfJ83b2xw8cwjxIOAUT/j1ddeJkk0F+49hRd2qLsFRsdETptWM2RvVNLudDjY6hKWHuPSJ8lcdBHy0Nk6D5+dXP25Xq8RhiHaaBz1HWzw8rFlhnHJ+s4+RTmk05oiiYe0ajXGQuD5HkJIlPIp0LiBIo1TlHJv1+5KPXnZpO57jLMSKxRZrnF0RhDWyYWPHzVwrGI8hq/+5dcxeUmZ5EyfvIvZ+QV0WZVTmlOz3LjyFr3BCCFd3EAhlUO9MXnGm4wyBnGO8Nrsb12nLApKYTAFNNyQ7t4t9q5+izgtEcMD/vKPf4e5hSU8qdlbvcKpu5/g65/7tzx77QDr+Pi1iMbcHEEU4B1BO3J+fp4XX3yR1dVVtNbUajUuXbrEhQsXqNVqdDodwjDkB3/wBxmPx5w9e5bf/u3f5uzZs5w/f54zZ86glOLg4IAbN27Q7/eRUhJFEVE0OURv/SAGx8G1gqIoGcYZYZzihBlemqKUg9aGQbfH9PQ8nuPSH4wYxQlZUdDd2KXWbCBGOTdW3+DkhYfY2BwxOx2S5EcIEp7HKEkohilJkiCUh+eFjIcjdtY3eP3l19ne7bK2tclgnPC8cjh16jhn7jrHxs1rNPyQsFGntBbHCavZDVuhQqy1TE1Pdk1q9Qgpq1KldF1OnlvgJ37q77B2c5/f+8yf8fLLr9Kou0xPz3Nr/Tqf+8wfcO+9/xAjNFoqVq9vsLGxw5nTJ5mZrzHuxURRhJQl+gjiFNZawjBECoPWmoWFBRYWFmG6zfkzJ1BKceqDH+DKG69w4sQJiqLafL0gwAjLzdVbPPPM15iemuG/+vmfxVqDNVAU+kj89+9p8B7u75ENB/T2hgwORvi1BsLxGGQp9bCO6wTENoGGw8LycTy3gdYpyTjG6gysYByPMFbRXpgmLxLG3QGUETo/AtVmrUa7WePU8RUwJe3OCr1+Qp6v88BDT/Lss8+xvdfl7oc+gjVg8j7dHZBK4rcEe/0+ceIyXO/yIx//OI88fg/nzi7gHkHBHkBhkVYjsAhTkMZDyrLADz1m5+tkxQBTOHT7MdYKyrJkOmzgKYfL71zj+LEVAj8Cown9EK01iOp1Sk1eRgo9j6AWIKQiT2NMbYGuiehT52Rrnq2tLa5t7DHjCmSZ47ZdHviuR2l12txcvcmZ03dz4fzdvPHCNzGFwAqNcjxEY5roxF0T+3H/h36EL33+f8cpCg7WLmFMSjKKKQtDsLBJPh4wOFhjeqlDv1egwjZr126SFyVGl3zly88zDkJy4TG3PE9rdo52Z5Zmo4XnTX5vfu3Xfo21tTV2dnaI4xjP89Ba841vfIN2u83jjz/OL/3SL7G+vg5Umfr8/Dyf+cxn8DyPp59+GqUUn/jEJ/iVX/kVXNdlamqK8+fP0+1OXlZrtdpc20lwBFg0rhDs3dhmuRScr9cZ9GJ0qfjmcy/z9Pd9BOVKOo2Aa8ka2WiI0QH93pjRIOad1c+zubHLRz/2MwzinDCc/NEXjse99z9InscoN0DnJWgN1vIHX/wTrm5s8PKV63RHOWjD33/y/Tz8yP0IoYjHCcPugAWlWFhaoshyjP4O5vkorMFRGKJLjQoVhSxpuAo71eEbz7xEf3uXBy7cRxjV6LTrzM09Thg5fOo3f48kHjMejbhxc4ssNyhP86l/84+5tbZKo9FiY2eTQhd8z/sm86PEEidjAtdFKkWzWecHf+B7uX7pDf7o818g8BW9/S1+8Pu+l/6Zs3R7A/78a1+FsEEhA9ygQbPZ5FP/6l/xUz/1kzhKYaxB65LCTL6JvKfBezwccrCxTa8bk2YFO70RV67fIi9KXKXQVmCEZbDXZX3tFqLImer4KFzQBuX7SAu6yHA9SbNZJ3JdsmRMEE5+LLZWELiaR+47y1f+7Ov4YUB7apaiyMhsQFTrsLa2yQOPjGlPLWIKl5n2Wfwo5NqNHbq2w1z7OKs33uR7PnSRpZPHAH0bbjTpUbDIEghUdWiyAkdJjKg0PWWZEYqS2YaPyTKS4ZBhd4AvHZwgoCwNu/t9Tp5YwZQpuqzkod6FtJkjPBVpXuCgcB1DqCxbpSSvTzN37C7iXKOCOssnz0LkESpDbzSiM7dAkmbs7u5y17mLdNodGlENXyqE46CRhJ1Z0iN08WudJZ74rh/g2lsvEo/28XwXIy0GgSkLShQi6lDmBbmj2Nvv8cBKh/29EePuACeqMR4bvMCh3p6m1WzTatZpNFpH2sx++qd/ml/+5V9md3e3apYf9gDOnj3LL/zCL3Dfffdx/fp1vvjFLzI3N8ej/zd7bxqjaXbd9/3uvc/67vXWXtXVe09Pz0zPTA9n43A43CXRDEVKMk1GpERKkWRJgUWBjiAHQhDYMGJHshaISiDbchxHifbYWiySoihuQ3JIznC27lm7e3qr6trf/X32e28+PNUtxhCgt5BgQAF9gPrQH7pw6lnOc++55//733//zR43wOHDh2m1Wjd/n7UWz/N4/vnn99U26fd6PBtH1AMH13No1provMDr9DlelO05qSTPv3yNg0eucudty9QrHnMzTa7v9NCmYHd3F9fzsEpy4cVnWD5wnAcfe/e+eu+93XUatZDRaEi1WidPS2tCjGFlZZlvfP0JBlGEsT5ZlvLSK+c5ffouWq0G8wuLoA1+GFBoix/W9tSqBVmWs58JvTRPMdZS6IKcgt7VHn/5mW/w7Lcuc2R5BSM8kIJCJ9SDBko61CptPFEhoMKglTKKIobRgN5uTppY4qjLxYtr+yqaxgi0FqVxiTVIAZ3dLk9+6ykqqsr19R18lZNlEc8++y3W1zdJsoxLl19mZvkw5156am+UMGNjc5PFxQWMMWhT/LVYcYJ4XYv3oD9ie7PDYJigfId6tYbneuRRziBNEVkpgEDlXLhwAV9ZlGiS+i4CQ60h8ZWH1QVZVJAlCQJLpRqiPH/iPHRRkHY2eeCOY6SDEUmWcPC2UyRFgRpqTp66m69+43E6m+eRRlOp1rEqYJy67PQE00fu5cWzX+Szn/tD/u2/XeGn/9FPMbtQtgf24x/pOBIpygOyLM/wvAZKClwp0PEIm4zxrcGzmoojibxSOq/zjDjTfOWJpxiMRpw6cRQ/CPdGKstxo6yY/CFIck3N0WRJhlSSYGGZ6vxREuVApqnWarieRyEcolGX3PpYGZJ/mxP5eDzGWqg3m+RGMzbgT82Q1eoT5xFFEcun7scJq2yefxKpSjeUIrdE/T5S1XDcGmHgYx0Xp11n3FvHdUKqzSbd0RjlulSqIa32DGGlirHQ73bwgslX3lmW8f73v58/+qM/YjQa7Y0ixhw8eJBTp04RxzFnz57lzW9+M67rlge+1epNd/bf+q3f4s477+TOO+9kMBhQrVZvqjCDYHJ38Efvu4NBbnjh5UtEO2N2gwTXVeRFwum7jpKaAsdzSLTk8W88Ry30GAyHaF3gOB6+Kqex4iSjUq9idcwr577BY9/9fkaDyYt3M1REow5FoUnjgmG/h5Tl3+rZgmQ8QFiNLjRIOHv2HB/6gfdy6cJLHDx8GCkklTxEW4vjhURRjO+HKOXcVK1OdF/QZEWOQKCEwxf/8susXdpiql5HeSG9foyV0MDi+wF5nlOrN4iFQhqB74fkxtIdDHj5xVWitAPk5RRXNvl8dVEYikLjORIhIQg8XM9lbXOLVqVNr9tnfrrK2RdfZhjFxGnGpbUtUi04ff8b+fqTzzIcljZ0WzvbtOdmSNPkplH1pPG6Fu/tTpfeICbXBc2Kw0P338Pc/CJfe/YcW7sdNIIkSTl950kefeAePAeeeeopXKWohwHV+pCZ+VlmFuYY9SOkBdfzyqH5ffQ00yzna48/SaVWY6ndItrd5sJrqxy8/R7Wvv55Dqwc4Y2VH+OXf+tXuf3oEtVKg5cur+KamDN33cMdi3fy5OOfxqRjfvXXf51ao8LHP/Hx0gdyH5GMc2zeIcsi8jxjNOjRbLaJbEprZhpHQXd7u/y6S4d6rYGlYDAakuLSiSIu/+XjfP4r3+TjP/nf0G7XKdKEaNQjSSbveUdpjjKaoBKSNo/y7NUd9LVNfMfS6eSElZCDBw9SC0Jm5+ucnj2Et7OBWp7hwQcfAso51U4yIhGWXjKmeeAIxVQb4U/e4x2Mhuxev0w0GtKPDY0iJc1zTK4ZrJ0nG3Vpziwxu3KAwe4W1XTATqYxZoyy0JpqELaWabSmaU9N4fkVAj9EqmJfBLaiKPi5n/s5PvGJT/ALv/ALfOlLX+JnfuZn+OxnP8tLL73E9vY2Z86c4TOf+Qwf+MAH6Ha7XLt2jTzPUUrRbrfRWvNrv/Zr1OvlFjmKIk6cOMF73vOeifP49Fef4433rnDbkSajkcvRI7eRakOWJjz77HnWNkdEGhyvytpuwv/2+5/jts61PpgAACAASURBVNtPsnjqXbzrY2/lX/3TnyWOE3zfYbZVJ8tiVi88iRSGdmvy+5LnKbqflv3mWFBRDtYafCX54hf+is71TaaaDYbbA6R0Wbu2jkufpRlBMVxFCEFvUK6YHdfFGIgs1MK9D9mBYxPlMc5GiD2V4rlvXSDtO7z9Td/FIIn58y98lW53zDCOaMy02N7qMI4i2u02geeT5Ia0sHT6Q6LY8Mlf+13uPXOc4ycXUW4DkU6uwP3Gk5/j4n+8gsIQ+B6NwCXwfA4cWeHccxeZmW7QzQ1fOPsqSZqT5ZqTx46ysbnJZz79KTwvoNvt0Go1+aV/9cu89e1vIwg9tDbMtJq849E3TZTH666w1FZTa4Q0p6eo1ercfUeL+aV5/uIv/gqpXOIk5bH7zzA/P8U4GpKmGcLziaOUMPRxXRdrC4zWpbheSBKdo/bRJsiTjCtXrpRF8c67UH6daqXOYHuTen2WQXfA9MIJdkY5X/zW8yjK3d33PHo/J48dxqHgx3/q5/h3v/mLWB1z7PhxHHd//W4oeS55oclyjdYWZUAqB5sVSOWB8sitRLgu2kRobbAICgRxnrGz00VgqVdCVlcvIe0cjgRhYhSTr6yU69GJc1wcslqASUaIIsGSI7d28FtNTGDJw4D6dJtoRyNb06SZJk0KKpWAbqdDluQksUHW2pipWbJUkww6E+dx4fyLTFcqdHc2Wd8ZkPkFSlkcp4RfqTQlz2MKJCqoMN68hhFuOQUT59RnZ+l5daxykMrBDwIcL8B3uanenSQcx6EoSsHTz//8z/Oe97yHT33qUwRBwNe//nXuvfdednd3OXjwIKurqyRJwrFjx/jJn/xJlpdL668wDGk2myRJguu6bGxsMD8/T6PRmDiPPDd87ekruNIShh6aVY4fXmCq5nDixBGefO4ilzYGDBOLRSME/P2PfILm/AmEF+IEdepOlWpV4LoKjCQapyTJmPn25FNA41STjgdIAfVaAylLQZLrOKhKlZ3+CC1vDBaAsA793SEzszWyQt/0F8WCKTTGlPLxLE6Q+9iqOhJ0oXF9h053wPWNLoePaOaX5hgMh4yiBOUqdjsd5B7/ZtAfMVYx3U6XrZ0O4zilMBprLNtbfZaWpxkWMcU+/FbzbEyit/AdDxU0SZKYwwdOYCk4eGSed77zQTq7XWq1Blvb20ghObSyyMbGdS6/ts3zL++gHAfPc3jt/CWWlpeYmW8xThKEXpn8ekx+6f6/h3EFU+0mYejRajZLh27PZ2VxgTc9eB95UeA4DksLs+AaXM+hVquQRDFJYfGDGTzXQeflPLPcY3kIKyjyyVeacTLCrdRYf+0yfnCRnW7EoUOHmJ5z2dzuMDc/Q+jBwvwBVlcHGClwlcQUlssXLjDrHeF9H/5RshxePfdp7jp9DyVdt+x6T9rzHkYxgV+2TErGwojCCKR0CVxFnGoKXKxSSCWwJsfxq6Q55FrTHQypVkKk43LuxRdI4h7tZo26J7H7kNlu9nrl2GESM7h+gUoaQR5R810OzLgYEUHvCunAY3VnjaA1w6G7pwiFQohSedjtddG5ptMfkluQWnLbyqGbXJBJYnVtFbG0xDjLSArLxc6AqapDJTA0ZA8v8JG6HFdDOOTaYARYxyVo1pmbmcXaCl5Yp1qtI4RCyJIdEYaTt288z6MoyuerXq/zyCOPsLCwwCc/+Um++tWv8v3f//34vs/S0hKj0Yhms0mtVmNubu4mQdBaS1EU+H4J6qrX61hr6fUm90qseC4PvOPv89RXv8xmb51ech1TpJxcbqOkZWF+ius7IzrW4DkSYyRveudjbO30MUbxT/6Hf4bOwJge/9P/+HNUXAekIstTwsrku8QoMwwGEZXAZ2YuIMtSrBRYJbGei0YBHr5XfnCUcHn++fM89tj9ZWvELeXgyi/fEd8PEEKRR6N9iad0kiKERGqDQbHZGfLKxddYEQtoazDW4Dg+UZxgmuXiKE1jsixldXUNDeSFLYVs0jAYjFhb3UQHBt+Z/CxieqrFCbGE4yhUKDFFgTslOX7bUa68dpnZOZ/p6Tm0KbAoikITJ9cJwoS771nimbOXqVVKQU6UxgzjDpXcMEhGJFnrb09gL17flbcqaM8vYPOCKI2Jsh6hqmANtJoKzw3wA5/BaAvHcRACTp++nWgcUaQJ9XqVvMjJowIhy1KZZxmBX6HYx0rzwmtXmT5yimFuuXD5CsvxgGj3MgcOHCZXS1zf3OVQbcChw7exuvoCWMgLzeeefJqj7VkWrmRcfWWNAyfP8H3v+xj1isOgex3XrSOEIKhOJpCRjsdOd5OiSDFGU6k0QGR0dzc5sLxCIQIGmSU1UGnUkI4gMT66KDBYKrUGMzPTJLnmG89e5PmXrrC8OM3pE3PoIuehCa/HThRzcrGKJzKacogXCjwnxGpDGmtwFEme0PZyRjphfu4EM8tHOLhykDAMeOKJJ+h1OtSrFfoCHnnzWzl65iEW5xcYjyfHBbzh9D088YXPUa1XmGq3SbwAbSzbScFumpOvXuRAP8aOdtDaInyf3a2E1Z0tBHDo8HFaTg23UgcrUFIgtEY6LiKdXCxUr9fL8a69eXfXdbl69SqXL1/mkUcewfd9KpUK1WqVdrt9E1jlOA5Zlt0U6dzgnQA3AVfuPnZop+66nR/+2D/kX/zP/4JGTRIqh16q2dhY51f+8YfZ6HS5sjVComgur/CBH/sJbGFoNxsMRwX1maM06jUCDz7wX/8oWbTJzvYGo37EpStb3HfbZKbdT33zeba2t5mdneHqVhc/cDC6ROIuLM7ywN1HefLJi2jH4iiFUDlf+tZZvvjNb9Fq1Xjs0YdZXi4P5ZTrs7l5kU6nwx0nj2Ot4c4Jr8fzL55nYWEB309ZPNTEXwgZ+Qmn3nice7ZWOffcZSyWU3cc5qXnrxBFEXmWkWQ5aa5xXJfC5ChX4nmSrEi4em2T9mIV409eP2qNgNmVkyxMzVH1a8y1Z2g0GlRYoNft8g8++A7SJCXNMoo8J80ydBqT5Rmj0ZgHzryRT/7G7/Ha1R1UINhZv8J9bziA3O0S1iZ/Tl/flbc05I7E5JBnGRtbG4S+jzCGNI4xnocpPKJxhHJcPNelMAbPd6lWfJQoxTJSOaUyURuMETiOd1N6Okl0ez2k5zC7fIBsPKQ3HpHHGWn8GjMnDiGUZXd3DT+oI6SLoDxI1QYu72yTGZ+hsKz1L7Ky+Fb6nWnGAwfXjUDAyvHjk10PayhyS6ElruOilFf+OB7aAtLBcX00gunZOXZ2dqgGU9jOAGTCsOriq5LhPU5zksJQbHTxfZf52cm3xTONKlmc0GxPkRSG1EKWW4rcIqWP43n4YR1hIpSQzMwvMDM3T7VWJRqN2NraYmtzkzRJOHz7KY6euou5hWUG/S6XLl3i4ccmyyPLM5YOHuXV868SptBq1MBoGkFALgXFIOLS5XUOtaHIcpQjCcIKM2GI74WMtMSplb1cP6jgOg7W5CVSeB/ihyAIbvJJrLV86Utf4hd/8Rfp9XqMRiM8z7uJhdVa35TR35hK+XZ5/A1MrFIKpVQ5zjlhfOjDH2a0uwPmAFPNKbK4oOFblk7fTnN2kTgziK2EdmuKNz7ydr73fe9mPBzRbDcYKk29UsHagk6/4Oidb+Tqxac5PneEmdkmYTD5qz+7uIRwPer1GtJ30FKQFRlpYTh2aIE7bj/Gy+cuMc41xmik45WO8zKkMyz4yjee5uGH30ClUuHK2nl6ux1MoTly9Ch6H/flpZevceVqh1qtSlBxOH3mNPV6lY3tbaQjaUw1GI9GbO1sMhwPSJKkvIdC4/oCYzKkKttLlWq50i60Jgwq+0GssNvp8MpLL7CwME+tUWGlPcNMq82V65dwRI2trS4CRbXWwPcdqlhCx917Lgz3ndE0W8v803/+SXZ2u5Bo6spn9uhJgmDyttrrWrzTPCGrguv7FFHB1vYuvpR4SqIsZKoExGRZiu95GN/DCQMKXRC4lbLPLcuXw/c9sixHJwXROCLchzz+yrVrjEYDdjtdrndHHJxpYtOU8TDFrl5l/vAB+t0uXhBw+6k3cPHCc+RZgkCQC8tq5zqr3XUqlQr/7Y99H9evb+IoF+V6WGMnLt5r168z1aiTR2OSRCNkRpZp4iQhiiOkUoRhyM52B62h3Z7HCJdxlJAXOTVfIUxOkRdEaY62BUluEGwT55Of4C7UQhQueWbReYxynLInF3i4VuIGCgMMYge/MU17fplKpVJej6JA7eVZr1Uxnks/SoiurlL3FbOzsxPnIRG0Zmc5Xauxu76KEYLG1DQ6y3C0xm956EqV/mCNOMpKteEoxdYL/NkqvXHMTEvgKAffVUilUMrB6BzpT94maDQaN/nbAH/wB3/A2toaH/3oR1lbW8Pf+103zBlujAjeKPb/5b9vCKf2U7gBztz/EH/8J59m+dhh+r0xtx1vEoQOWuc8+PCbOfvM43TignvOvJnv/4EPk6SCIsswYsj6ZgrC4nmGmakKtfAU83NtTGGoNer4+3hfMltQbdWoNRooV6EchVcU7Fy+Qnt6mrvuOMJXpiusrw8wplROGmOQQuAIh9wI3KCGU61x7pUvo7MCneW8xQkp9ORnERcv7lAUG4RhiB8YpqcO0h+MWN9KeeXlC4wGGtdz6Xb7aFtOvihXIhwHhC3PlRynfF6rIQgw2pDlmmwf7T0hFUZZNre3SLMaDd9F2xTlZwzTDi9dfRabFbiet/fBNvhOFakkruPguT5HT7Z57/se4d/96z8Foxl3+jxy92OM4u9QquAwydHrq9T8GoHrYayL54VIx0NgELJcwYROjSyPSTNNngxwEOSpZqbVKg+SMHi1KRAJUdpje3sVyPneCfP4y899icCTdHt9+lHGV154Dd9RNMOAI+o1ksEOjeYK04tTtI8d5PB0gy8/+Q3GyQiwCAknTxzj4YffxKUrG1zf6JQsbOlQ5IZH3v7WifIwwmEUZ3hBFc/1SLMU1w2YXarS7e6AAV1YrIbeTp/bjtxONxpTq9dBOXRHMUI6FDYmSQyFkRgDMwsrsA9GwrDbx/FCqtUq1cCnEpZ8kzzLKaKYPB6jPBdlC4yaIZxexOQRhC5h4DEc9IijMQbJ9UHKHTkcXzpAteHtq+etKg1cXNywTmN2AT3ssrt5Dac+TbPWwttzVRp02ySjIVmm8ftDpCMZSkvDc8mGu8hKjbFNcf0AicELK/tCfn570bXW0ul0+KEf+iFeffVV1tbW+JVf+RU+/vGP/7+K9Y2VtxDipqjnRrE2xiD2xFM32iiTxGubsHv1HL/zm69y/1v/AYY7yDJNUqR0M8VXvvkyswvz3HPfKd729nvJjeGlV7dwfUW9CWQReZ4wHhQMUom1UzjSYXdzm8MrkyuBvdBjfaODVYJGrcHG+jara2torYmSmLnFWR577AyX/uPjFMKh2apjbYGDQZgMUThoHIx0OXD4GJ3OLi6SYZTsSw7e6QmKQiD6KUUx5pIbIQDHkzhW4TgKgQThUp8uJ1myLCfwXXSeIPb47FAyb+QeyXNzo7Ov+eqciLYnUXjoJGP18hXajQZ3n3iAL33ra2zvOrjCx3FKUJWQAtf3bj4HvVFMnkSEDVhearC1u8OnvvIEX7vyLA+cfIDvee/HJsrj9XXSQRDFQ9IkwREO0/WpkossyqkRLaBWqZDGMTvpiFRn5ElKK6zieYp+NCApcgpX0qqGDLMRvWzE9eEO8T6+4Bvrm8y0W0TjFCkVVkgSbUhGY2TgM1N3kHLIodkjnHv5AsNoSLUa7hVvWDmwwoEDh+ns9mjUOniei+M4OMrF7sM6Ji80YeDjuB5eEJIVBWle0BsNGA5GYAyudGjWq4SBT6vuc3l9jVGUMIrLnYDYk9QroYGC6WaTaNhnc31yBKrvKhJtWd/apVVzEKKB53k4rkumCqwUFAJqtRqp62GwhL5LGkXkRcHu9jY6z0AIKtUaMzMzVCsVhDCMx5MzNAwW5bpgobCaanuJNNesXr3Kwn0riAKMzgmrdRzlMhr2aVIjLXI8pShNhgyOEoTVOsp16Hc6+NUGzj5EOjd61jeELHfeeSdRFNHv99ne3mZ1dZXRaHRzfvvGD3CzQGutKYqC0Wh08wOWpilCCB599NGJ8piaabB85DaE9Dl2ZJqH33CCzUHMy69eYWHlINapU6k1WV4+gjaSItVs7SYYq8lsxmg8Is9zDh5YJu7vMDvdZnG6hrYhh5b2UbxdDyXkHvyswPMdZmamSJKE3U6XC6uv0un3aFaq9KKEPE0xnqJwXEDSrlSoBD7RcEi71cAPHNavre0dVu6jaOrS1EIbQ1FYXLNnjJFKXAxClqAppKKwBs910bbAWIVSLkWhyU0BUpDbAinFXu/esg+NDoNRwqvXr+NKD6yhWQ2Ynp+hNxxzdWuV7WiNIlHkWUS9XicMQpo1jyzLcF2PFIWnDLWgzg9+5IP89u/+fskiEoZsHyYqr2vxdhyJDS2ulOjc0IsHGDRdBFKWKrSWzOju7rI73kUGDkobKjIgsinjNCKX0BvFbKQj+oN+2SpQOfF+pk3imG5XEMURaRLdHAW0QrC93WV3t8/VzXVik3P2wiv0up3ywRAgrKXVaJOMM4SRbG9v43keruuipFt++SeMKE7AGMZRgu+XlkxKKUSusdbF9xSOsFQqLoGnsJQeiMZatDbU6zXcIGR6ZpZ2u42jFHOzsyWHXLf/9gT2IvQ9osgyihM8VX4soGwbKN8t0be6oD8ao1VCv9ensIYiN3h+QCX0ue34ca5eucyBAweo1qpcuXKV1kyN69fXJ87DFLpEDBiDtIJKvU6lfheVapNod4fW3DxFbvEKD+sZqvUGke2jPBeswFqDkC6eF+C4HkJKWlMzJElCvT75tMmNon0DLvVd3/Vd/M7v/A69Xo+PfOQjvP/972c0GrGzs3MTspRl2U2f0SzL8Dzvprjn21snN3rlk0QaZSwdu5MKOW968D6EzVmcrtGfqXNi+W188S8+xfUrrzK3dJhXL69SpJpCZNQqIXXpsbzYpll1qQQevuOXnolJikTz3ItXefvDt0+UhyctngShc4pkjDQZFU+hrEMUp/T7MRtbfVAukoxkGJG5DpnUZIXh+MkWvu/THfUJAw8hLLOzM/szQACSNCoVxNpgjCrphEKW7RApEKbsKedZgRYGhCVJE5RUuMIlTTOwoDxJTnnfHMeUC67JvyF0B2Mub3VwhUPggfRnubq1xYVX1hmmfUaFIYslaZqx0R2gC81srUKW56RJSlQYTJ5Tc2v8yIc+QaXSYGN7C8d6wHeok06tFpIWKQKD73iYXFO4MdL1MI6gcAqujq5Sm65QqVVI8gzRDrnU3yJMA/IkLrkdgY8eaJSSeIHLMOpTb0z+cj44N08/STB+iBaKvoowQiL3+LoKcIWk+8oLHBBwfKpNRRcoC3UVshR6VAKPaq3K9NQ0YVglUA6NZg3Xm1xBZwG0Le3QkHQ7XZTnYrFMz7ZxlERJSRAGuJ7Dem8I0hCECscP8cKA6h6ZUMlFursdxqMurrD7mmroJ4aqJ2gutgkrHuPRiCzPKfKceqWJ1jkSiAqohDWiOCKJelSrTeJhnxOHD3Dvnae4fOVqOXGRpvh+hc7ukOFgcvFD4FdwfB+UxBpbjlwKQ2PhANG1K3zlc3/F0soCi4duA+kRhIKpmQNIoVCei+f6uF6IkAKEJIkT/MAnDENGg8l3Is8991yJxt1TjzqOw3vf+15+/dd/nXvvvZezZ8+SJMnNQ80bLZIb8+E3JlVKTcJf34v9QIcAoiQnbB3l3JN/xh9+7KP8y1/6VdLcMkpShp2IH/zBD/Pv/81voHVBnML2To9avU4lFOjU0tnts7EeUw2rOK6iXq9zdW3MycOLRKONifMYRik4HlrIUkUpBE7go3yPKMlANbm6GbMxjEiTFGk073rP91BtTvFHf/gnGCuIowjXVShpEUUOzSpJVpJCJ408AcfxwBiEERSZxnEEriuRFixl2yTPE3KdkSUaXUhMXiAoKAoNWJzMwdjSoc94lj2r2ImjHlaZqTcQRuBVPJICLq5ukCUj6kFAo1LDrUOaDEorPaXw/dqeC1OCcAzWSLrdIX/+pf+dxnzMwBY888zLLNUmZwG9rsXbDwJMZrBFjhKg0Ujlke856BRALjXaFniOxPerxCYnbIQkWYbje1TCkKlmi531bYq8AAWBClBi8j/lXz/6NvrjHjYZY/OUYVxiLm2RIoXGEQpXhBhlUELjOj5O5zp5HOP5Vebe/T5kEKAqPjSbKD8ErXGmptnP+5lmBVPVOkJKxuOYzBSUCoZy4iEtcrI0wZOCqDcgzVKkozDGEvjlnGgWl7ZXrWaL0AvIVIkMyPLJE9nqDjk0HeLJAgqJ55Y7CUcqPKkosgKtCzQOXhCCtYxHA1rN9p5bumJpaZHZ+QWefOpxpFSsrq7SHw/p9foT5yEdhc4LAifEytImyupyp7Y4v0j90cd4+ZUXcTyXarVBXmiU56GkpEhTKrUWIEBYrBDUWyHGaNJ4hLMPMNV4PCZN05u9a2MMU1NTfOITn7hZnOM4vtleucEuueGGFARB2UZznPJgd2/cME3Tfa28F+fqXNvYpdo4wEsv/RnffP4yBTDdqvDofSe4HhiOHlvhiSe+zv1veidGCPoDTbc7olpT+NVZmk2DtZqZ6SZFWhIanzl3njidPA8jRInUtZYsT1GqNAUvQyGVi3J87nvgfl599WW2r68zu7hEbapNozVVznXvtfd0bvdMsyW6MOXHYMLQ2mBMOUonhVP2uOWePV2Ro4s9kZuxN3dNGBdrLPmeWcYNJxtjDTiU/8f89c5okpiqtDi5fLQcmJCKbO9ZSVWdQDlMhU08xyVXGXEck8UZzfoCMlSYqiYqxmRZjjeV40jFyoEWBw6c5OqVLiuLSxPnIfbzMN2KW3ErbsWt+M6I/SAfbsWtuBW34lZ8h8St4n0rbsWtuBV/B+NW8b4Vt+JW3Iq/g3GreN+KW3ErbsXfwXhdp02+9vm/tOefeZpnvvk4ru/wwR/7KaxwUEL9tUoNeVPZJsRfK9iMLQCD3RvnsNwYvbKYPdzkg29+50QKme2drh0O43KkDMgTjXIsjisxVpb4TEBJedOVRiixxxIW+IHHaDjC8z2kEGhjMLo0mw0Cj3arMVEey4sL1vMNRg5ozxdcO6+wmaUy5ZLmC7SquxxfsrxwuSCLXApbmtDqIsJxHBamahRpTH+UgHIJKiHYAnCpT7c599zZifJ4y4NL9twrPd75puMsToc88+J1Ll7r4QVwcqHBS6tDHr5zmdXuLg2nZA6fvdRltmUBl8wWXLgy5v7bZ0EYlOcy3wzoR5qlmRa/9B+emiiPT37u/7TdTgfPlbQadUBQ5Dm1Sp0kSciLhErFJc8ydgcjjhy/m+XpJtfOnyWPE5xKgJUSr1rHGYzJs4ShSZmuVXBrTb73vg9MlMeph49YJ/DBSKTRRMOEpJ/h1XwOn5yi4fokY4OZqnKlC5tnL/LxR+7lw/cd4YsvvMZtP/3fs93t89uf/Swvj7pobbEGrFvC+y/+r/9+ojw+9pHH7H3vfYx6s8Hm2hZ/9ekv09/pY7RmfqVB3I8JRYijFDbToKGuDFkU09veodWosLE7pjey1KvQnmlyfbvDyr1HqNTa/O5/+MJEeURJ3/4fv/jP6K++xtGlGVYvvECoFPPtBjONkALFuFA8dWGV/jhidnGZH/74z7N84jQ4FVDqJnno28clv01tOlEev/34U9bzPOIkIS9SpuZmyLVlOI4Yji3Wir13MMD3fTzPu4kycBwH3/dQAjwlCAIPqQRKWDyhkUJwuuZPlMd/94++146TbQQucZzi+iVDxZiyPqRaE4YVrAWnENiiIAhcKqpGkUsGCVzb7DA900bpMZ4XYIwly3JaM0v8yv/yf02Ux+srj+++zLj7KqtXX8atVBjuXiLNU4o8x/f90qpKlimV87OlussCSnpI4aCUQ5wlaGtxPQ/HcUv29T5CSkkS5wglkEoSj8f4gSQrFN3emKmpBo6rCPfG0IQCx/f2OAgZJkmxQmIs5EUB1qKUA0Ki1OQjaWmSYnWBqmuKQuJ4impDEsUSYzNGY835yxlpDFKFKAtpbiifdYlUYIWDVD6WHFeklOY3zr6QsEWW4/mCJC3QeYHjCBBgtWGq1cDbiLi+OUAoiVaScZzQarjMTMH6ZoGVoJySGW2MpV0P8QMXEQ/RZvI8Diwv06jXSUZ94nhEGATkWUrquLTbM+giI4l6NJtTFMqlPxpwcHEB2ZzC9wrSoqAwBuFUKEJJ5rhY7aG9CpmenG3iV0Jm5hpcv9ojzwuWl2bouBEqkKRRTrfQbG53seIE1599lXwc8dL5C/RaDpFy+c+f/yv+01PfQld9wj3+tbYazw/Q+3FssQI1ZbHVLQ6drPK2+EFqXp2dzS6vXbrASqtFNtJEg11G2Yis0Oi4h2sEo0FBt9dBSkHoerQbFVypcYzDeFMTupPT66TQXL18mQdPn8YjZ313RC0IqVQFTasYpZqNQUzFFXheys6rT/F//5tf5eHv+QHOvOXduJUGiP14TP3NkecJrisJfAfPLz+ErnSoViv0RmPyXCOEQagCg0BbgXS9EmOgLcoIhBIYIUqNhbWlUGifqWlV4Z3v+mmMtuzubvP0uT9DW0thDFmS4siQIvOphBVcxyVNY6zUuP40RuQoMeD4iQV8FTLcVigh0eg94dt3qIflq89/lsD1uevOFS5evMIrz3webXKiZIzvlRfZ9R3SNCtX30bBHknQWInrBXiex25nF4TE90OmpqYJKxWsETz0lndPmImlMAVmj2ewubNNqxmSG8Hlyxucue8OPE8RVsLSqVqAzkppc5Hl7HZ3cF2Paq2G47jlLkBblDT7muMtjEQZcFTpiG2EJM4cjPZLCzBrGEQQ5QphBULV0wAAIABJREFUy1VKlmsEEsdxEQZAIRyFRVEqFQqKLEUUkytOp2o+rpuB1dhC0wxdXCkIfYc4S6mFLoMo4Y7jU3QHGcqBNNf4vsJ1NKk2KFmKHpQq/RY3N3vML7RKr8MJI0tTfNdDBAFWZ6XgQwmMtNSaTWyeQ5aS55bAC6k4DvPNOa5vrpEMd0sPUG0hz1DCYoRBCktapEg1uXgq7qc0T1TYdbscPr7EHUfvxQ2nWJhb5Dd+41+CEVQPH+Wlr7+IzQucIOCZQnAxSXjLez/Eh37vDxGNJrWwUs7lWwuZoRgO0fnkLI/xeMwX/vyLHDk0S6BqjNY83Nk20/UVLmZXGOYxcZRBWuApn8UDC6y/8ixRNKagQPkV0CmOzJidmsZKS69RkDgp4dTkHzMhfU4/8jYeftvbiDqbPP+Vz+CYlG6nizClFmG7P0YK8LXGN5q1y1fYWL2+L1zE3xZpniEzBQisgGIUI6RLWmiMFdg9O63CANpicw1RctOqznFdpFAYKyi0AQtWWISSsA/7sWpljjP3vY9Gw6MoYq7vbLK+8QJCxIS+S+g6uI6LzQQ5itBrY9IxcW5AWoQx6DQlsZZarU6WpwhASYXgO7R4OzbntdeuUguaLM010UkPKzSBA6YYY6Ui12CKAuk46MIipSLLMpQrQXnEI009kIDCmpzO+jZBRaH34dnoOg4WS5Zq8jznm08+g6sKvMDld/7oK1QqIY1qiO8phChdWWYX2riOQ2Ek1y4+xwP3nyFNYx544D7OvOE+EILAd3D2YccW+BKLJB6HVFpzFHmfJNJYERM6GseFzEgqQQ1jFONxhDEapI9wfHqjBOVIvNAnL0ISI8iLlFqQ7GtlNRjEKGMJTM5gJBFW4MqShhaNU1681KFVDzipYTgegwjoDxOiJKTS8tlZ65VmFVYgjSZNU06dPMz6Tp9+MjnbZHt7jeb0PF61SZREeI5lNMpI0xH92jZpPOLqxZd55fIrrKwc4+7T9xKNI+brs2ymESKPGPRLz0jXq2KFwBEeUipcf/Id0dFTS+Rjw92njuBYn+9/93u5cm2V6VaLN73hMT77+JcZPfMKh5fa3P6Gh1hYXuDHP/QjLMzNghH8zMhy7vIF/vA//UlJXQQkliLN0Pv4uB+9+zhPPnmejWfPc+DoPMfPvAHl1lieXcE/+wxKuviOQAU1jE7o9ja59+G7CObq9Ne3ufzNl+klGfXZFs3bZ0iGYx6+/wS90CftTX5fpHD44I/8BAiHdGoGtzbNcJCwsRlxIDfMhpoDUz6p3+b8pWsUtWV+4p/8c06eeQjrBv+/rLoBupFlXOwxSVAkgzWyJGJqbhnlVHBdiRUCqRyUtDhKo62BPEMAfWtxVcnyaTdLJKznlDL7/YCpTN7nlz/5j3nH2/4eD93/Rj76kY/T6V7jpRef5Imv/DFxnpJkOc2gASZGmxSBRcsRxiYEtgnKliC7otwt2wKkAted/Fq9vmAqbTh66DA6F6RxgnIEQikc1yWKIozJUaqUFKdpWq7ahMD3HYSwuIDvuiAgywqsFlR8D50nOHLyP0XrgiRJGA1jpBT0+33SuA8kHJwPaLemEMAwyRiOhwxGMZvrl/Bcj43tLvOzDS5cuEyjHnDh/Pmyz5VnzE63WFyY57YTk0lcHaHREkb9FF2kZFHJ9zbWMtcOKIqMHIMxBUI6gEViQUgEAmMFnhRIZTFaY4SD60j8wEMzeSupH5euRX7gI4UgSRMMhqrrMNcMsUB/XOA5gsHYAjm1ikEgiMcFeVGqIX3PwxU5rXq45+8nceR+2kgxuiiYmZ0lj2M6m2tIaYjjEb/3+5/GFgX9nW2O3X4czxVcu3qVB+5+O53eNnme41rD9MwMc+15Br0hRTLGCx0cx9+X8a/JE9IsJGzXOLh4jDge0Z5dwnFD/uFP/She4PGnf/znvOGBe/nYR3+EJB7SqNfAagok42yMCEMq1UU2nv4aaIMxGp1nePv4iGz315k/sgipptvts9Pd5fDtx5mfn6VZa7OwuMLatQ0KqxlEfaqBTxrF7F7q0KpVOH3XYVIXskZIHjo4oceVtS0aM0fx08lxElLuPXlCYKSLVgFjnbMzzJhueEgvZBCNuLa7y8LJe7nnoUc59dBj5W5QgNjzl/ov4wb3fFLSYqZBaJBWYIXkwqvnaVR8ZhYPgiqhZFKVO9AwcPBcB+WESATGWqySuHKPsSTEzVZHgSr5+RNGmqZs7Fzny1/+PBLJ/W94I63WEnff8wgvnvsmO7ubeyrS0jDGWkN1ao4sH+MIh+FGTrMdYmwJQbuxM5BC4rqTv7ev78pbKqwpQTCe51CrhVhhUa6DcmS5QnJ8irxAG42zxwVWSmK1xlqzx08u+7RSCgQW3w2wdvLBmaIouHTpNXZ2tpmbm6deD3BFQjWssry4jKsko8GA7YHhjjvuoLCSuiup1WpcuLTKOC63iMYKLrx2hYuXriKl4JGH7if0J7dTSuIY63rkWQFYTCGwe4ceXihxjUs8yLFpinUkQjpYU5QF3JbXzQpBVhgspYuJ0QLPLe3lJg3fCzE2w2hDbgX1Sojnxkw1Q9qNKp7j4DiWdivgtoMt0qIgzktZvNaCNDW4rkfF80AbHCSeI7FJjtgH8SfwPbq7O/gqpNVok427bGys8o0nHufCay/iOy7D3hBczXA0pl6Z5sn5rxFWfaSAnZ0tDh29ndnmLHW/iiMjxsmIZrWK3o/HqTa4umBnp0/odTlw6CRPPPEEs3MrzLSq/OzP/iyVSpXm9BxBNWC4u0uUjGlUArLuJnGas9sb0Vw8yGryeYzRJadFgtlHHu2FBi8+12d58TBZBtcvXOTpbkJxvM/K4gqu61GkEdVKBd9t4XuGUW+D3u4udqrFXfedIPMF19KU1Y0h0lQwgzGCjJVDk5t1CEpom0ZglUOcFVhrmGpPoXyXDElsHF6+usGb3v9DvP39HyQTXglyo+SJ7Lux/DeF0RhdYI3Acyzr6xscffRRrHAR0iJFiXmtVlzq9Rqe6+K5IUUOUZKibYEjJMpaskwjHAeNQCQGsY+2CQgOLB3h2NFjQIE2RdnWE4Y3v/m9PPvsV+l3t3BsiotLWGnx6Dvez8bGOi+/8CTVYIzAMBoNbwLMtNYYa8ud9YTx+q68XUGWxhQ5uI5kp9cp+bpWUhQF1kKBpFnxyLMUrXV5YGktruuX6FW3PLRsz9ToD8fEccJcs8Z+Ho4rVy7zzSce59Kla8R5znve9U7mjx2kEjg8fe4c51+9RlEU3HvmNFPTc3h+BW0sjXqNI7efYtDvEXgeIDGAkJKrly9x1+l7aU9N7oSRFhYlLZ5b/v1CltZvWMtgbGhVJbYIkb5ffvACiZtpPFcQBBLfgyiVJJnFlTlRPC4PGwuJlJP3VqWJCaXDVD3kwsaIw7MBxxdrzLYC5mYCTh9rs74zILCK3ECuFFvXNYHam8SRLvPtKsdWphiNcowQpBlkurKvNhI6Z9AbYrRmutXmc1/4U65eeh5POUxVaqyvbaJzy7VLq7x87gJSFnz2P38aHOgMhhw7tMRb3vFdvP/76qSxIcNFSI/hcIh0Jl/R3LZ0iOefv8TJI0f4gR/4IDvdId968pscP9FjvOVx5LYT/OSP/whnz1/i6afP8tChOTrra1SFZfuPf5PPPXud+UPH8VR5LnLw8CEuX3yNKBrdJBZOEi++tMHFx1/h1ehpRuOE2VYDs7DB+OI2D77pjbzw1Ne57fhRUp0jRQvPs6ycuY2r167Rnp1jOyo4/8Jl4qQ0+j2wsMBtD8zQ2enT7w/3cWMkIHGMxg19THuBQecsDzxwD/E44dK1Nfq9lNwU/MVn/hTXd/h7H/xhrFVoA0bC34TtuzFhNml4SrK7vUmrNcXG6iUiqlxLGly5OqLiG+bn2jjCsr11lcD3WVmaY7FRYzjK2erF1KZaCFeiHEmSWoQWaCWwGtx95CGF4fqlDZ75xnm++7u/m431DgcPLXPs+DHml4+yMhiw862vYaIx1cYczdYi8aBHnFqePnuN/+pdbwGTo65dY7DTwYgcqSRKSPx97Jhf1+KNLW9YJQzJkoIg8Epqm5J4XgXHddAYnP+HvfcOtu2+7vs+v9336eX28sq9rxd0ECCIRpAAKZAgaZKSLJGUnSgaRY7GShRLmvFkZCuZZJxJ7Ik9KiNrZMmFaqZYTJAEwQYQRCOI/oDX73u3l3NPP/vs/vvlj30BycokOdeageUZrJn757tv3X3OXr/fWutbkORzBYSmowntbUigqecJQ4nUdXbaXXTL5dVzb3DTybm3IUGjRKVS4fbbb2FicoqN7RaFfAEhUvqDAVtr11i5usTs3DzbO5t0Bz2q1Tqz0wcQQhCEEVKCkpkjtlQK17IyUXlBtpwaMTTLRtM1LM1GhdltWmkClUpEnKJHGgcP6Oi6w1Q5x/mrffzIIo4zE9U0zQR+XBvSWKGRYmiKqgnpPm68tmFQnyxjWxphmOI6OuW8jWmaFHJ5XNNEKIEQCWVTkVgm/bKNlALbEJiaYKxo0+71GS+OsdPvoaSBpWvIfVAJZKrQgSQaUijM0WztUh8bo9No02kPME0bRJiZ2RoauZxDv90n8hPKeZfm9hbffPTPabW2+cgjn8uUAdHZ2mlgO6N3RK7pcObMIQ4fmmVmaobHH/8211bWuPX295AvGvTbLZQwOXF0Ad1uUO0tE4yXSbeWyImIZrvN1Dz4Qkci6A2H2ewV2I8K6uz4FNeNayg7plgo0o90mobOZz7+COtX3+TkkQMsLBzgpXPncG2bklvl3HOvkRop3X6bUM8xMzfFwvFDKEexcu4iuZqFXp6lszX6IQICoRRKpcgkJBz2GQYhO60uly5do9FoITSDWiFP1Fjlj37nnzE9M8uZ2+/GyJVIU7kvl/j/tzA0uHLpIlPT02xdu8y1zZTGa9exTAMr9VhcOETeNhg01pmaqFB2LG6cG0PTLK6sNmkOEyrFHIWcS6WcR0ehC9CkZG/7P1ooiaLPTmODP/zDP+A3fuMfU6lU+OHzP+To0aNcvHiNfi9mvj7DBx/+BPliheeff45rK+sg4PDiIroS2IbN+f7zeH5Mkr5lX76P57HP5/fXiiDMNr8ykdi2hRBaJvqvZ3ZBCIiShJxtIlBoQicK4z25TYHvDXjj3FXcnEN7CDudAWHUZ2xsDGMfEqhxHDM7M02v73F5aYWVleucPrVAt9ciDoaoOOTg7BQdr08YZfjiW2+8ic4gU/4TInsHNV0jCXx0zaFYKLytWjZqvDX2QWSzfJlKEplSzNvYusbcfJ3ilIZerHC0VCRIEtbW9UzsXmQChJYGpqmRCB0zp2Mbino+Znc4evtVK7kU89n4plKwyOUcagkU8g6aENiWTj5n0OqFJEnmAq6RoAlwbIPJao6irdHuRZTy2eZeoAgDjzAe/a1NlaBeqZCqFMdy6HQ9vO4uMkgIghihQ77kcuL4Ma6uXKGWz7EVbWK6FUzToN1oM1mGy288xfrtd1MrT2Ma2QEi9qEal4R9asUcEkWlPsns9Axj9Unm5w/giJB+exOvu0OhXOHmQ9NsP/o1zl3epiQ3ON/ooSIP3TTRc1WEgmFv8JZg/Mg5AIzNFHHHS7Q3m7iuxVi9ypnbbkCokMhfx8gV8QOfTqvJZq+LfniB9951C1HYZnNjmWanx9HTR1neXOPwe26iUN+iu90kKVgcOHBoH5lkru+aUAwGXWbHq0yXztDcbTLo97AdB93MMTlegKBHxTH5/f/jf+aODz7C+x/5cWYOH0WIbHTy1xHCU3tjk5d/9ALt9VVazjE67QjXVqhul1RrUXAMFianQCjW19dRJw9QLVXQdZOd9oBu3yfnWBxWdSoFByfvYpCi5OiHWRgFoIXESUBjp8Nj3/wGv/RLv4zrFBj0fT780Ee5euUiBVunFyi2uzt88auPEwZDlq9d4ZvffJxDswcI/R5hNNiTpzXQhMZ+Hs87WrxzjksunycNExwrh7JjkgSarZBXLy7T9fqYuoWRppw6cZhqLsYyEgSSKJFUijkOL0ygmxb+UpPJsTK2U6bXbu+reCdJwsrqOo2dFlNT0wy9LkoJTp06w7nX30DTDV4/9wbl+hhjEzUG3S4XL51jbm4uQ8BYGlKL0XWdMOwz6CcUivk9GcrRT/Bi3mbY74FUCAEHpk083yKMBLvdkLnQYVKUqflDruk++nyeedvFDxw2G122NwbUKiaOaYOekKQSTVe8sd5n1xu9UJxYmGR7d4jp2CzMmyipUSrlsQyd7jAi72gszFTYaAdM1YrkCjYD1ySX17BwcGsGaSIxI8WJxSlef7NLveTQa1m0u6PraJeqY1y7dJ4DBw9g2zb12gQrV5eYHhsjCLqkCZTzRYpyi5tnpyi5iomcSyeAK2tDokinVijh2IpXX3qWm++4D0vYnDl+OoOXjhg//eDDCGKefmOLV154ka9941E+9zOfwzLBtGx6K1eZK5VprF6iVp9C3v4hCl/8PJ/4/LdJbIPa6eOoVHC8qNMcG6PXC5FmDi1MUfu44U0emuQn/ruHiYYJ5cokf/gv/gCzlPDK7qsYs1VWr23y5tIqJw8tstRt4hqKucl5zPxR7njgk8hhQGP1Mh994D5+5ff/TyrTJ5hYOMJkroSbjN6pAggkvd0Nnv/WVxk3YpRucNPdd3Dq1FlWN7ZpNtv0d9aYn6hiaoqd3SaXn/0mrzz1HX7uH/7vHD17E+g6UuwNYf5Tanga45gpQWuTjetXKJ46Qm9rjcTKEQ16rGKhcg6mBgNXw+/vUigtkbd1wv4AQysQxJBKyRtXmhi2TU5XjNkxaeTx/vm7RkxE51uPvbS3a5L8+Rf/jCee+AE/99/8t/zWb/82hw5O8NnPfYYHHvoU7UaT5m6Tv/vZz/HmhXMksc9Xv/oVbrnpBNVSHhsT2zZIU0kUhQTB6Pr37+zCUtfJOS5eEnLpwi6hnhJFih+9fo0XL20SyRhTaVgCLqwOuPHwFHffOU+1aJOgMInRnDJKM7n5hhymbhBFIZuNDuO10dviMAoZDIaYhkWnu8vxowtsbm7S73WYmppExopioUJ9soqbc+n1ewhN0djdQtMN3HyFVAliKVEyZvX6OtXxaZI03Vfjo2R2a47ibAnj5g3QwW8lmJrB0krGwlIlh3q+TDfeIjVihDTodT1SlVkymaZJHGUsUaFDe5hi26OjK3Qh9hxgYgxLEIsE13Iy0pRSaIZF5A8QmqDX80iGHncdriAsAykNmr5P5CeMV/NEUqAUma2TpijmRy8SqVRMzcxSKtWI4xjLtpifnyMaDvcMDwTdTp9NEWFbCr+U49zKLrlikeq4y9j8AXIueIM+M1MztDotJqpTXF9ZYTDwRs6jduJOLMPm7947zlf/wxfQDZ3Fw/M89eS3ueveB3j5mS7VcgW0hMbWGmm/zz/50lNork3RdomCENsQWK7F3MwkrzSXMs6CplBy9EP13FPnuPUDN2NZJruNHsdvmMcqKfy0S6rl6ekw8HtEb76Oa+gEMmKj1WHCGUf0OviNVTo76zSvF4mbBsNoyLWkQ/H4HHOTo7noABkeWgMpE0yhOHHkKINEMXloke3Bm6RJhEg83nvDYbxOkyhVGI6L7boEg4B/9y9/l4/97c9wwx3vQXcdUOI/CT0YxzGLR45w4aWXQSraa0vIXAiWi/Qkke5gIFheXuea12PYaVF3XTQ5pN1pcPDMLeQLNWxDRxMCzdIxdZ1uIqhXqyPn0Y9DolhmzRSKOIrQRMLA63Lihltp7Szz5S8/yomTN3Di2FFyOZeB1+fGs6eZmqjwz3/rn/Hcy+c5ffwwJ2anSYRAiAwSTfFvKFTQtm2iKKTRjvjzx16kMfSRSjFMEpIUdE0ghUlEyuWVBls7AcWayy03LlAqCFZXBzz34iXcQpmH7rkB0h6ddoNnzq1wy9mjI+cRJzHDYUAUhMxMTNDvdfGHfbxBn/F6jXq1zu5Oh/pYiThOmRgfJ5Uxg2HIxNg4RxcX8MKUbndAv7lJp9VA6UbW8uzjSznwEwQKN2eRswQbO2FGalCKFJ3hoMvOTgenWKWgw+LkFJv6MkoFqCTBNCyiJCVKY4QSoHRUkpFnUjH6mEAJQT7vIFOJoTsZrC1JMWyTwA+IowDbhO1OQG1+mtuPTzDnemhuASVMmp0OQmiYjk2SaCRJmhlGuA5uMvpx1mx3GK+WaLe7eN4Q3/cpFIrs9PogFJrQMCwHaZSQrkM7EORrdaYmKiwemmXl4iVWVzbpegnvmZhnGAb4YUgUjW4IAfD7//IfcuT4Gf6rn/1fufu+hxj0OnzpC3/EztYlPv7JzzE5Mc7q8kVqlXFW19Z4/pVzeEGEmSsihcmnH/wgD979AKVyDv8T9/CNx77HH//Rn7C81EHbR9W6/OIlvGTA1MQ0tipSmxrj8OnTXH7zIhur6xyaPsTB+hwXvv8iiZbQ8zzWNpapT5bZuHaNi698i35f0R1sUbNqvPziq5QLBSoKLv9whTsf+a9HfyhKkC/VOXrje2heeInpcoXxqTkeHJvktYLJi888wXStRFNGdD0f20pJohBkwo+e/T5rK9f5sY8/wsd+6qdxCuU915v9hZQS13GIoohCIY9eNNhsryFMB1tZBLs9DFmjWzJI+wNsJVi6/Bq2ETPoNfGiIdX6NDecvYGj8/PEUiNKYagK7IMmQrrXPqg95JCm6/T7Xb7+9UeZPXQGXbPwBgG/+zu/y4MPfYAPfOABJiYn2NnZolSb4tM/9fd49tkfsrF7nWMzGiqJs0PfdtGM0UvyO1q8q9UqnuexcKDE0ROzXHr2TfQ0c+pACEh1NCVJ9BSJiYhC7jw2Qdze5emnt/jSj1bpBhAlLZ595iI//eDNDJubxL7ihVcujZyHEIJOp8W1pesMhz4/87nP8NqrP+TA/BwHDhzGG3pMToccOXaEXrdPEERMTk+QpCm9bo/vfvcJjhxbpFYbY8cwsfNFgiAk3TNEHTWSOMG2dTSl0W5HhJFkcqLCxESeC0tb6IbB6soqMwslfnCpRc7Oc/rIFH4p5ZbbFvjCn17hljsmaXoDVi5Bo9Gi76UgoVIZfVG41W1im9nuIYh9hBAMBz5SFtFNQcE1SSLB4QkNwxTMTdWYmV4gb5vY+Trt7WUSdIRd4FtPv4RtWPQHAUoJHHv0Q2RjZ5Mg8iGOOX78GO2dLbQkxu8PsC0LpYPmarRjB9vXiIYD3JKF32nz7NM7nPvBMn43IpWSbxz6Bgs3HEPTdyEM0czRO4Bq5RDjpUN86V//Dp3mFhdW17jlhlu4sLzBP/nf/jFeZxuRSt5/V4UTxxcZn5vl8moLL0jw/Zj26hbTE1VkqkhMycMfupf77r+djzz4CaQYfbZ616338PzrL1F15zDKKbtLG3zz3DU2rzYpj5fRdlI++qGPcvcv3UO9WuPz/+5f8cpXvs3LL5+ju7lDfdLk+RdX+cAdc5Smb+eO06fJOyaBnmN8cfSbphAKpI5pVZk+ejtLF8/z3c//AX6nzfsffIjt69eYLubRkwhdxQT9NjkzT6QS+p1dtKBJzjMJzz/Pd3+vyU0feoSJY2cx97GHAEiThF6/w7HjJ4gPzmKX8hiWQ7PVwfc7lKpFEiL0nKLR6JAEIZ1mD9fUGPb76JZkd2eZWk7yyz9+PypVtIYpz1/coOON/rlcv7aT+aWKzENT1w1006LV2mWiuoVjagwGA5566im8YZ+1tVXuvPNOTp6+kTg1OXLS4Bd+/n8g2N3hi1/+Mo31awy9dWzL2tde5B0t3koqbMvBMWweeN+NPPrUBYQm0VDIPSg/JJlyh5IUbfB31kHC1FiBJBziRxKUYBAINtdblOKICdvh4MkDo+ehFH7gk6QJQRTywgsv0u82kEnA6TM3c2XpGivLy0Spotcb0O8P2Hz8cYZDHylTytUSm5sbjI1PsLG+QbPZ5NDi3s1/H3MTpSQ6OlEkGYYppi5AJkyN5Vhv19DTgDPzJeJuHyFTCuOCE0dP8tKrS0g9ZO5AjlxeIE2dZRUTRgmaBpZQb98KRgmpp8SGj1Ihmsjm12GSogYSzRWInESqmLFKGU2l7G6sMTXu0o0jnEiiEJimRRDH9Ht9cm6OOE0JYok3HH2GB4rBcEDsDzl/8QJef4Crg2noWK6Nbpt4fpAtlA9OM3vgEGubDdY227TXe3jtEKTCsE26nV18fw7dBCsNSfaBn12+cAVnGHHnrXexMHcLf/61b9FrdtANxdT0FA/81E9gpR0aWzs0dhoQRSwvr2PZORAGL7z4IpcvX+Ho0SPkcy6ekrgoLNckDveRx/k3yEWKuO+R5iVaf4jWGDLrVonTmMX5WY4emOby0nXiJODA/CxPPv59crUipmXQ3u7g4NBs9Ji7eQzdUWjSp+vUKczsgxdBJuAkAKELTN2gbBuEacTa5g7ViSm8TgOvv0s07KHShFavx067y267x/GFRW47fYKaY9Jvb9NpbDG2cBL2N3Zn4HlEUcj4+Bibmx69/jaGaTD0PYIwYMqZBmkRBhGuJuimIV7Hx1egIUhlQj5XZOD12Ox4pEpnpxuystPFC0cv3js7zf8I4hjHMZ1Oj3KxBElAp91mY2ubX/zFX+SRjz3M9evXuHzpEo3tbW6+5Q5mxuaYLtr0egZ3feAjfO/JZ2lcDdGCdfazsXxHi3cSJ7AHtzt7agwBpLqAt+aAQpIosQdLgzgVXN9JaTQ7rG53OThWpb3exzAkwyTBC2ImHJOaa3Dq0Ni+cul0uzRbTQQaly5dolI2GfoG1WqNe+69j8e/+Rhf+cp/IJ8v0uv2qdYqmJaFZTtMT8/QbOxy6fwF2t0uSZpiOw4g0PfT9hh65okYxei6ougakAa0ml3uvOMUR+sKS3r80XeWqEyUmSzkePONTean6my0mxyYzaOiGNs0kGqIpmVTm3LB3Bc0S5gpytrTVVCCIAwlxi1qAAAgAElEQVSxdItExcgoxnQMIqnoJUPmikWmxmpsdkKe/tGbHJmtcXymhlnImJrdoU+6938Pg5RhODpN3zRNVlZWyNsWumGQKoHjumxvtChWyxxcXOCNi5fptXfRRY048Ok0I9avt/HWeygFVs7g2K1nmVuYpZjLEyeCYrFIEI1eNE+dOEauVuDl5Reo1sa4756TWLbAKYxz3/33UizVaG42sHMutfE6a6vLmJaNrmWfZxKFvPba65w4cRxd14mikDgOcEwNOfrjoFjOc88H72dta51oEHGgNotbszDNAl7ikddMgiCg3+4RDEI62w2m5qY5feoMretXuHJlm9nZGputXd475lKc0pF+kY31NaLm6Av+FPaYvWrvYiVQEuoT02x0h2y0O+hxj8VCSsG28Vy4duEakRK4+Qp33XYbJRuG3R0GqU+SxhlpaZ+xvHSeVCQMWg0if0AQtRgOPVJpMHfwGIfmD7C53sAbSpQETeiYlosMI3RLR8oITZN0uh2++cNzKKtMbxiztNEi2QfFMokzdvFf/AWKdO9CuLq5QavdJQoD7r/3HnJ2kce+9jgf/8TDnDv3Kr3eLp/69M/SHwTohTLVtEi5toCMW2xf2MLcx6XrHS3enr+DQmCY0N2Ouf+OQzzx/FXEngQsCIQwODxXw7F1+v2Q/+urryB1g/lKCS/sMWYJ6mM1vEGTzX4PKSx+4tPvQ4nRX04lU8I45fraGnnHpV6tYpgFksTkD/7g9+j2+qSpIpdz6Q+6aDp858nv0up2AMHBmRkKeZdCIU9tYgzHdMmXKiRxSroP498klaQkKBSVnIEuoDmQNHo7WCsh39djPG+I0ATdAZQcl49/eI7dtkdJFrg22MERGpGrU8o7dMwBMpX0BjG2MXoepw4eYbszIIoSik6OxakyvW6fdq9PqIUEQUAlX8DUDW47uchAxrxxfZU3Nrd5daXBeLHGw+87wdeff4m2FzChKYbdIUGUyeWOGt1ej2azRZAzGQQ+heIY165dxjQ0oihhc2uXIyfOsHzuVapOjuuvXaK1LnBwefgXPoUqxFTG6iipE8cDKoUy1foUF15/kcmZuZHzKJdL5AwX6kVUGHCgXifwh7jK5vrqGmfPTJKvHWZ34zLDMMBxcyzMjrM9CDkwPc32+hovvvgSn/rkp9ANcF2XNAm55/YTvHZxbeQ8Tp48wfbaGlPFCtKPcefmiOIIM2ehtgW16iT/+t9+gccff5Yzt5xidqHGeMnmjXMXMXIuix+6jyvPvUnHNOhbHXauNvB2FZOHE65eHr142/gkws0uVWnKbmOdtcYaEwcW+fDf+UVeee5p0vYasrtKGIQYVpFf/R9/GqEbWKbJysXn6XebdHp9jr//Yxy99XawjX3zLudzLaIwwJ1Iwcyx3LRZWd5m6HtcfPFlupsNwiTByOcI05gkjtENhWWCYkjoCZqNXQaVcb7yrZeIjDFk7DPsN0iCDvz8/SNmkl0ujT2ZijiVSJEpjO60WuQclzgK+eTf+hhCKCzLodcZcOrsaWbmFvi3n/8dpNQ4sngSXRTJJ030kkVnbA4tGV1z5p2FCroFFJCoFMd0Ob44xrMvrRKmKQiBJhSOnnB4ooaKQhZqJdqtDn6q2Gl3OX16jjvmahSLBTRrlmqlTLVWQhdhJsk6Yrzl9u26Lq1WiyCKiBPJ+vo16vUipmUh0PnaNx4jDENK5TKFcomD1QpRHLG1s43VNajX6xh5l3yhiGkaJElCmo5+iOi6IAxSbE2Qt3V2uzEIDcsQCE2nPwywdI04VYRRzLnzm9x6wwS1qsX6VouBF9OPEiZmipkAkqHhODqhJwni0U9w27YZq5gkaYKtaRQKDkmqaPY8dM3BMgSmMLARvHp5Fd1Kydk5bjp6gMEgAIq0vQjHtrDDhCSRuDmLfhxh26O3591OB9d1iWIfz29RKFXodYaUCja93oBeGJGYDp2mx7rTpBdF/OTP/RyKmFy1yGZrGZnE2IZBlCR0O1003UWmEn8f45s4GhIpcPQ8mmZg2CXSWAcp2NzaZna6wfXr1zl7coGttSVEmpIzBcHQIxGZpEPg+yDUHkFFQ0rFvbed5YYTo+neAAQDj7wwMDQda7yC1+ujEoVtaPS9AUs/eonLK2ucPLNIpZTH7/SZKBWIfUlqadjVALdkMXf8KO1+A1MMuXBlBaNexspPjpyHxCRFoCNR8YCtq6/iuBqVyXEOn7qJyvg011/6PltPrzKMfDS7QK/fIpdzifyYYa/BoN+l1feYP3Eap1AiFqN/L96KodeltbHFTClPYaqEjHVyZo2x+TqDvsf6Vhs/CrFshZ9EGE4BTQoMPcEyIZe3OHHiNPl8mSuNDoM4IfGbpN51Ku7o74tSGZ1GKd6efRtaRsjyhkPSOEHu1QEhEpIEnnnmada3NrFsi4Uj01y6dIXnnnuSUyfPYugKr9kgjobYxt/Qmbdh2RmLbs/A4MaThxmrXGRtt4eGwDEF99y0wE6jQV53sEip1yqs7LSJhM7ceJ47b5xFKYmVy6FbNuiSJAxI99mG5fM5DMNAaBpBFOAHQ9yc8xcSnkoSpRK3UCBXKNLqtEmVxLJtxicn6fY7rG1tst1ucuTYMVIpM/LOPmbelikIBgrNFnjDhIxHqu1pcisUGprI9MdzOZPFxQqeH5BzTVKRsrsjqZVtSqZGuzNE0zJCjZ4axPtoA5v9AUmqYesChGKz3abXHeIrhaMJcq6DpjRcxySWKUksyDmCkusilMKxLFxLMFYqoAtBGEU4tokkIt4P2qTVoFgoEgQJKCiP1Vg4fZarr52npBKGg4CEVXY3O1QLLnYhj1XMNM23dzZRKiWIBoicjmnlCcMEPxhSn5xE28fh7hgWtm2gK0m300boEMQJhmagSXj22ee4/4EH0AyN2vgUO0sXUXFC0B+wu9NA1wR33vEeLMsijrOXO00SJiYmGZ8YvWjl7BKOadMNPabnc+gaNHd3iDFJpcag7+HaDkJLWb16lZnpMSpHD+GaJipvEscDDh04RGglPPPsOe68t8qBMyU2rlvEanTopMQAJCoJaa0t02vsUqiMM334BLplMTk9RTIzzfVwQLe3hZUrs3L9PKblYBgGvc4u0TAkGUT4oY/MaFN7etqMTJHf6EZsbvXotzzmTUkSlwiDmFLZ4cDcYXqegQg8jh0q0+p3iTWb1naL0BsQiwQhNQx1mGLeonf+Ml0vwVQeNx0vkddHn3kLoWU57xnF6LqO0nSQ2d+SJAlKZjpMSgls28G0E65ee5PPf77Lr/7aLzFRn6Bndrl44TVMy6LZ2ISojV6ujZzHO1q801RiGCaBN0TKiHpe55d/7v38yVdf4sL1TaQm+O7L18jhct/ZOpvtFmMVh+mxRWama3zq3nGUNsTQTXTNIU0SVJpgGPr+mFsKbCdHsVRmMPR44/IFxiplji4eRjcMhGbQ7vRIpGQYhCSyje2aJGFImiR0vT6xlJTrNaZmprBdiytLlzh17OSe489oEUUa6Bp+AlJJUiVQuk4aJiQqxTBsdFNRLbvccvcMt5/OY6YJ1zd9LjViTp7IMZYTvPRqG02A5WgkiaRayuP5wch5bO/0kClIkbFHZZJ9KTUUnW6MowsMTSNKDTRdIUyBClPiOLvtp6nH0s4Ay4YxYRMnBqZuUHHzmcrbiJEmMZoGtVqdQb9Pvlxg4ZYzHDx+jPMv/IiSazL0+swenmN7t4dT0PjSl/6MszffyszBYygkYeizvrlDuVzBMEwSGSMEtHcbI+fhhQpdJAhbwylWGAxCDCNBSsXy0hIf+rGPYOgGKTC7eBNeZxe7WKJe9+l2Wzz31Pfxg4D+oMubr7/I3NwcQb9Pt9NDz42ufROnEaXxKlUnj1krMD6TY7FSp+d4VE9ZnO7ZNLcH+K0Sw6FHMIg5c3oRz+vzwssX2e23OXygTmPgQWhy6ZUUQyjOP3+dfexv0VV2g4xVBIU8n/v1f46bK2KWxvjRE49x8Ykv469eJhaC+bs+ysGb7uPobfehhUNe/d6jvPHcM9z0wEPMVOr83q//Bvc+8mne++M/w9jEdLYEHbF4P/v9l9BNnXisTM4T6KKH0FroYpavPPosWnESadgcURrVYh4/0bBrNSqlKSCmPwi5/MarvPrSszimxpyVQSUuv9TN6MojxuHDE+xs7SI0HVBoupaZjqgEy9IQUmWoEaXQ0JBRxDAYcuTADOOTJX7l136dYwtHuPXmGzh6Yp6e36OYFGktbTPU/4aOTYaehyZ0wjhAyRREwlzV5ec/fiNvrhwgNTWWN7d48vur7AZtbrlpjtnxKn7XJwoTIj/FLuRJ0wRdvdWWZFq8o8pKZpEhWyanptF0jcGwj5AyU/cTAj8I8YY+x44dy+BAmqBYzOMPfSzHQu6xKE3bwi04mKZOLucyDIaZxsnIkSkpKpUhM4SuZ8pomkKlAkvXsU0TFSc0VmKWDJMokmxsSTbaEYs3TNHptACNIPKxpIEuMsq92IemSBD4KASOZZCmgn44xDAEBcNACAmahjAyvRShIBhGGELQ9wJ0S0PPC8IkxjJMUgNMw8LUdISdoonRD1XXthFSoQGFQqYKlyhJoV7i9HtuAl2wcmUJRzcZm5zC83soE8qVCgM/IIxDLMtANw2Wlq4yNTWF52nESfwf2W/9/8XY1CGi4Q7CstDRsRwDzQBDt5jMpQz9Ie12k3K5hOkUqM4scvc9d2O/dpHnXn6FKEqQAh77xjfJWQkH5+cZtnZYWdugWhodc55ISSwlUovZaF5h6tQBrGJKY7lBtFvEdQ5SKifY+Q5By8N0HK5cXaFac1nb2EBaOZx8lYqw8fu7JLHNmZtOc8dtt9Nvjn7zRoAQOpaTZ3y2gEAipEasNFaWLtDeXqFmSnLTCzz0E58lN3MjbT/i8ivP8Mw3vsT4zGEO3XkfU2dv4Kk/+zrf+vdfQJua4eFPfgalGPndTb0eZimPJlLQdFKVohkafW+YLRzDCJUKri61sSwYBjEiFTQbkiTxUUJi2gb5Yp6ya2JkYkuohH2p+U1NjKOxJ8D9FuVfZX3znrJc9ti07C1MkgRd11je3AbbYXbc4fLV19ncWeNn536eQmWKfi9AmRr5YmHkPN7R4h35AYZhoRmCYZRgAjIZUC/Y3H68AiacnlWIxMTr+yzOVpipCvTJEpubIUEsEJFE0yRhnNHTM7s0hb0PPLGSkmKxyMKhQ0yM1en12yiZoilFHKcgoFavUqnVsuKNoFDMMxwO9+blJqZhkivmiZMIhWRqcorQD/ZVJLKDwQQp9uyRBIaeLUEsU2CZYJvZCb62MqC5HVIsGPR9sB2bnZ0By1sB7U6MYxsIBIYmkArEPvjHpqkjzRzdbhPDMCm5DpGKiZEYZmYbtdnqMFGvogsYDCNMTcNxXISeMhj6uI5FIhOGQQxSkArI2RbGPs5UXdNwbBvbdRn6QzrdLnEaIx2bXKlIkiQ4uRxHjx1FpilRFLHb2ubixcukhkupWkLKBBnFdHttFCn5fB7fD8jlciPnoYSxt0nTiSOFSFOkphMrSPyAzfUNNMjgk3UDtzzGbnOXYjmTFLBskxefe54//dM/5cP334H2nrvYbbZJ44Dm1ujFW7dzfO97T3DD+07RHHRprzRwS7CxtcrNRx5keWWHdn+IVm1wbWuT6cohLq9tc1N1kVvuvI3vPv0t3PIM+bLLzMwRfJkn0RSiHHLH2ZtHfx6ApvRMQx6VjQaVAlIWDkzhXXCQPQ+rOo1RGEOmEY6MEUnI1PwhjEqF2oET1KoHuf2Rh3n80a/w73/3t7j/oU9QKI6uK16sFDByNokm2Gp28LyU4TABmmhahC77yEjS3E1QusQPA04eOcbytWtIGVOqG+gI4ihmtd1AqBTbMnFti2Jx9O8HSUrOMfDjFLS94i3+nx2EECKD7CqFVDCUigtLV/nwfXdgF0yuXNvkie89zYmTxxAiJNVT0n1orLyzYxORZLhupeEYmRCLrmsMgoCc4zLwfCyjwIdvzbG53sFFYNqZmP7BRZc4DpEqzkyIlSAc+ji5HLpl7Wvmbdk2d95xJyqVpGmE73f3btM6pmZmNGYh9wRjNEzLQteMPWlaC2dvlodSxHEAKHTTpuAUMfaxiIlTUMpA6BqaymbmlqYwTR0/AoSgVtRZ3Q4g9kmigFZHEUkT0wgY+gGkKUoTDDsDcm6eThgjnTgzZR4xZsamkEaF6sk7SKRHv9siTWF8bJpg2MM0LTTDYHPtKv1OA9dx6A4SXEvguOkeqzImTlKSVJLPORhINnd7GKODGijm8uzuNnBzLnGSUKpUKQqFrgs2t7Mt/tzhQ2w12/QHfYZDH9MA2zapVMu0WjsUC3mkTDh9+hQg2NlpUCqWGPqjt6O9zi71+jhpGqEpnygOMEUeRcqg2+FLX/8Wp284wz/4B79GEod87zvfxXYcwt0epWKBf/Wb/xRdM3nogfvptXcI/AAvVnT73r7GakfvmqEwrUg1jcMnD7C5vMYwNPEGA66svUEiE8Yma+z2LRanT9JuDFg8ski+WMQYy3Hr4ChPfucN7GKeWBWoj9cp1QpcXw7ZvhbxvhHr919+szQyuzGFxEShOxWKC7fR2dng3p/6BfTCDFJJbMfglg9+hLP3PoTlmFknqODH//v/ib/1936FC+fe5Cd/8if5zGc/y2c/+9mR8rCqNsM4JAwS2sEAkiJKGiAk+ZJAyexypesasUzI5TRarU0cW6CUSRRINKkwlWJ6coxcQcPSNYSM9tW5F3M5HvzAe3ntwjJL15dJkhRNM8hu4ZK3tplvmU0Ild2+VaoBgse//Tzz83U+fPfNhH4Hf+t1DFNAGuMN/oaOTaRSCC3DiColUSIb/uuaII4jDF3HsE1QMcWSiZQhSuoIlQk+aQJ0zURpKtP6tiySOMZx3X3JKc5Mz1CJ5FvrYlIZ7LEjNXShg1B7zhzZWMPQdRAaSkqEpmGIrF3Kbh8SoYESOrbhkCajA3lTpdA1MHSBrhukaSbCJYCCq1HMa5iGYuhHODmDQAEiuwEVciaGDimCNMiYnVKlGKYGStsXdNJxdMI0YPX6qyAkMk7wo4jdnTUcoaEbOpGM99yLMqyvY9uZ/nosCJOUKMkkcTWhESUKgaRUzCH3Y0+naViuQxQHqFRRrdZYX1/DsS2kTGl3W+RzObrdHlKmFIsuSRKTSmi2Oli6jZAC27IZq0+SyxXo93ziJETfB/Bds2yEDoNBwJ99/UkkBrW8Tb1SIkkSqrUqS1eusLGxxcLheUw7z/OvXsQxMzPZzvY2iRAcP32W710+z/rKCvWxcYRdwNiHcJlyPMpzJfrDAUvXX6dolSnacPbGw9jlhJzrsn59m2sr1zl1wxkOHT3A8Mo1vCRku6NYvtjh+qUWx85WqE4UkSLm1Vdeo5ovkD8y+qn6VwubIGMWAhw+dSvFsVl0oShPzJIRDP6iC9Zd/S//Q8DAsAzO3no7v/6P/hG+vw8hJltjcmwczTRIE2jtJCA1lICiY4FU6IZOPpcHLXOM9/vZAhshMVyJa5m4loFpymz8gsR1TBxrdC2gqbk5FJLxuSMsXF+judt9+xlJKd9eZL4dKkOovNXFKzLH+nJpkpN3HM0UOKMQPwhwi6MvLMVfR6Lx3Xg33o134934zxP7B1u+G+/Gu/FuvBv/2ePd4v1uvBvvxrvxX2C8W7zfjXfj3Xg3/guMd3RheeE7f6osobBtC8MwKBQK6KaG0iS6rpMmKUiJaZqZP6QQb7tuxCpb5KVxgpIKP8g0N/o9j52dPn3P56O/8KsjbaU+9mMPqpPHTvP3f/VXmJ6eRtO0PXajIvL67G5u0tvY4oVvf5G8NiRvaJgiBZEiZIyXSIRMGKSSjbGTXFq+RqU+Ts/b5OkfPsnr390cKY9Lf/JLartp4CWSbiK47Nt4QUIkFR3dRZMaQqZYxKg4RAZ9rNosKEkaNNje6kEcsjhs8is/XqRy2wS9H63yW38c8LiW4/uPfnOkPH72k/eole2A2VqBG04e5ve/+DgHZguUizrTR2q0d1NuvfU+nvza12m0WviRopwzEUaOhZO38cAnPseB6TrlchE/9YjCFknkoWt5pLB43y3vGymPB//2pxWaAFOQc3UmSiV6UUTONUmClEDpGIaOkyriJCSRGrfc/F6CwOP6+jKOYTE9OYVrOqzv7jA9McnM+AwvnPsh7XaTf/NPf3ukPG69++NqbvEEpZLDwswMv/f7v8VYbZyJ2XnKxRKnjx9ncrzCwuEJ/vxLX+UjD32QVmuHSrXMKy+/xBM/eIaTJ0/z/LPPowuNtbVVut0ecRxhWiah74+Wx/1zKu/kMfQcCo1KOZOGKJeKFHMuQhj4YUi376HpOrom0HQJIluATxeKTJTy1PIm83Wbgq1hCIUXpnhxzPs+/b+MlIe//HtKGztEZ6fJ5vY5iqVDtPsDLqytMVEvUzZMxip1Go0m/d1dDh19Hy+9co5B6woffeRuvvLNZ6lP1LjpPWco6HlCf0g3GLC01iBNUz75yGh5/OYXnle3LJRx9IRG1+PRF5eJpQF7qDDHdRkGEZZtkyQJUZzxPyzL2rM5+wsEyFu7vrcYklJKfvvvPzzqVlsFQUCSRKRpShiGe9IYCX6YoIDd5g5R5FMq1ikXy1QrRUCiaQaWZaNpGrquo+v620bMf2nJOVIe76wZg2XjGAJd1zAMA8MwUSp5+w/R9nDbKIkQGsrQEApIJRYgkwRdaJlBr5IYKBwj08PehwsaxZJDHAfYloWmaSRhQGtzi53VVX7w/Sd59fyb/PDcq7R6MTKNyJs6vj8gkRGmUCxU88xO1JiZKnP7bQ+zeOIGwOTKtYvU3CMj5yHDhE4UM4ygFyQUbB0zb7M7jDHkniuPZhIrgW5b6KZNVbaZL2uUjhzkG4MlwpbPTA7KZ8cQOZN22yc0oayP3lR1Wj4TdZNzS6vkHYs4TdjaGXDrrWcZ9DzOHlvg2Se/zPzCDdRqfc5fOEeQaMwvHmV24VCmhSIkgygkjJokkQepQZB6hPHoLuW5vEmcZtA/2zFYb7VBwrCvo2kyQ9ToJvNzVXSRZ2pmkc1mg2ZrmzRJmJ6bp1wooWTCRLXI1OQkSRix22gQhaNDsKaPHccpzzJ3cI6p8QoTi6e4fvk6O+E6n/2p+5g7eIiCY/Dt7z/B2PgEP3jmGX746o/4mc9+hoce/gjX1rf4yqOPZlokUYjmFLDLdSy/jdBHR70I0yUVOmkcI1NBkhMolVGu00SCjJBxgmWYSBKkkjhWDsNwSBPwh9AzU9ycy0bLo5KzsXRBzx/uy9Nza7jG4dxtTIyFOCLPdnOAIxIO18dQ211CmXC910bJEpZTQ6o+gbdDoeoQJB1KpTn6ccBrly7x9JeXmJx2+ORn7uDM0Xm2d7ZHzqNgSC4sLdPyoBek1Gqze/LXinw+TxiGWFbwNow3ZxoMhx6apuHHGqnibTGsv4rJVmr09yWKIsIwQsqEJImJoijTAmq2+MK/+U1ajQ0CrwtaRvBLk4Sp6QVmDh/n2Jmbufuu+1BIdN3IYLia9vbPXyni/5/xjhbvLEGxx/lXGTROB5QiDEOEpiG0Pa0AmSB0I7t1xxG6oWMYBjJJkEmKUCm6BsLQcB2DUmF0GzTLdDl0eIFSuUwYhQz9Ho995+v86Nkf8tgTT9Dve4BGIV+mFyQMByHVyjj1SoG5qQnm5me57fYbWV47z/quxwP33M75Sxe46cQdnP7E6PZShm6QiJhUCaq1Gio18Ppx9uVCAhkMSqGTArrpYMQtDk9M0Rcp9bE66FDp9dDyGUQwSl12VUwQj/65SJWQJjqJlLyxtIoQWWfT7/qUi+OIxKJgujz99FOg5zF0h1LRpjY2Tr5cwtBiNC2H0DIXnTTNDtgoDgmGo9P0dd1AaAohFGmcYuoGuqGhIzCc7HsjpEIoRbGUp1wscG1jM8MVWxaaoRGrBN/3GCuVqBVLdOMmOUvD1EaHgvXbG8zPH0GlMX7gIZXFzNxBdto7vHH+HAtz87hOkYlqns3lq1y+cpm+N+D5516g1+6SSoVp2Sj65Asuwi1RqIyxe609+ocCJFKhwggtFQgMlMogejJVmKaJRopuWBhSEaUZ9tp1bQzTIQwSRKKjhEEsIREaYayIwogwTtH2cdkZeiFx0CKKPZxKhVpqoUcx1YFPovJsbG7Ru+BRyPsk0YCQGDGIyVfHKFUOkIpn8bwBmlWg0x2wsnaVwyfHEUhy7ujv7eWVbRLpE4sChp1HDQN0U0cIRa/XwzAMHNvCMk2kTDGFpFRwmZiYYGVzGz+M+Yvbd/Y7xZ520H5Qd1LKzG0qzX6UgvW1FR776h+zcv4F7GIdQ9dIlUAohaXb7G5eZX35PM8/+Q3mDhzg4Pzht3+HlPJtwuF+4v9m7k1jLM3O+77fOefd7157VXd19Trd07PPcB3ui8SdVEztomQnjhTYgWMoBhLEiIHEQAAntmAZMUwJkhzYEJ3IMrVSIkWKEsWdHM7aM9Pd03t3VXVtd7/33c85+fDWUPqWWwgw8POpPhXOve97n/Ms/+UNTd5FWeAgcRwfay1FUeJJ54eti3IcjK0qLOTr6UvgOJV8ZJHnFHlOHse4UqDLAq0NWIPrHsEdvDAsrSxjJfRHI67eeo7f/NznuHLpGqkpkELiOxUe9ZPv/Sgriyu84z3voDPXZnlpiWcvv8TDjzyG+Z7HuD9mOBqzvn6SE2vrLC7MrivuOi5ZPiXLJfVOgCogzacIGaKMpsSAMRhrKyldx+HY8eOsLM0RdydkRcmc5zJXc5Eu6FSws5exYzxydQQPSyXpHYwRWPZHY0BQanj5lXsE3i6ekhidghBkusDg0mzP0enME9XqKFWpIBorgDqIBCNykjQ9Eo53Op0ibKWN7Pk+buAgHYW1YHTljSlcyW5/gldbYJKkxOkI5QpC3ycMfcBUHZLnIOpsh3AAACAASURBVID9/n0sOeYIjNO92zc5c+IxMiV47tYl1tdPIbWguG45trpBGPi0Ox4nV0P2r+8QegVLiwu4yuPFF17C9XzWjh0HYyGPcet1HD+k2WqQpLNfZo7rkucFoePgqRDhSlAW5Qqi0MeRlV3dJC+Q2kFIgeM6SGlxlEQKhXAUSakJhSDJDWWWU1AgjkAH990aO5tXEcpDuYpWOMd49zbp/S4H1+/THcXcuDum0eki7ZhJmSB0AGVGdxDz8BMbjMYZO7tjhLJ4QZ372wleUGDM7AbVmQyRjkLgY6WqCEP2r02/y7LSsSmLDLAYa6jXIjCaWhRSGv03hONe/+PouuLGVAlXl4a8LHnt8iX+9A8+x+bNV3AdSZIMcKQDRaVZJB0fxw+IaiFlVvDbn/2XvP19H+bNb3sXzUYTKavc9XoC/8+y8vZciTQWXeb4oYfnGRzPYEU1+7HWIB1FqQvMoSqeFBaspsg0whaMBmMatRZlnpDnlROedBTOUdT8fJ9SGxyl8CLFy9dfpDcYAQpMjkVjlKa+0OEnfuHnWFle5ea11/j6t75daW5EAZ///H+i29/mwXMX6Q36ZFlFmrh87RXe/84PzHSOpCzxXZ8s16SlZmekKZSPFApPlmCrKjwz1QPN0oTLO5rN3S6jJGFU1KgN9mnPCbK9EQevDbh1x7Af1HHM7Ml7lAkSLdGHVN5a4PPhH/04t25d4+r1l5DCRUmP0ijOnjzJz/7MZ3ju8hWOnzzF0uIqrc4yjh9RWoH0AdWimE7YP7jLeDSZ/cHokjQpkUoRpzG+9g6rHIMfeAAUpWb9+DpO2ODe3n20MSgpqdXqTOKYwahPEic8cPJBrt+7ydd+8B2kqEZ1s8b1l59jaWkNa3O+98yzfOzT/zUpJe97//tZqit2t24w6CUk+zsszDdwXMOrt2Ku3bzJ3v6Q+3v7vOOd78DzG1x65uu4MsNO+zSCkkjNXvI2G3Xc0CdQHqEbsLSyQhQEFcnEsygrcRQU0qBKC1JihEGbSoo3cD2csBKV0gpibUizjCRJcYPZbWx645L1tuTGrW0Sk3NxeZ6l0x+h1eny0rf/BX0jmdQ1r+708JREHexhbMKFySKmPyZfbVD3XR5sHePFlS7PXnsFf34VrxzgerM/l97eNu9+8iTdFOISctxK/OnQ31QKwJRVB0+1K9M47HUHGKAWBIdyrpb/P/QWrUuSJEGbgn53yGf/+f+EKyuDdRCUWY7nKNJBH9dxycsJYyFRjosX+vTub/O7v/nP+Nyv/W/801/9HMeOn+R1JWmlFL4/27N5Y23QdIHrhLh+VEmvihgrJHmaE9VqSEfRT3Kef+5VhsMJSkkunF9nealD5DfYuXeLH3z7+7zj6afxQr+qyLPySHoiAFmWUW/UGCUjrt2+zHg4xnGcqhV1BaYs6XQ6FGnOb/zmb2GsJR3HFXurVkMIEI4lLxOKWDM+NWVlZRmh4MzZ2WferufRrAUUOkNrzSQz5EhkmaNk5V6irKHAYmy1C9iPDUMkGJccgSMs83MtPOUx2dlD5y5WGnxmr6ySrCQvS6QQaOD0qTMc7O/w2vWrWKqqWjgKZTVZlpJlCQuLS9RrtUrD24+wSmGyDK1LtBbkRjJNDPH0CNYx1mKtRvB6sjV4rnPYmtpqWV0aGvUGeV4QJzG61NRbNRzXoygK4jhGimrctLW9RTKJ/3qXcoR45YUfEPgOntBMh/dYWVrh4ZMLfPFP/h82NtaxsmDv1j2OzymatTrnH+iw1dPcuLlJkaVcu3aNPMmQUjIZ9Wh1llHCpShnp8fXazWiZojnOITKpdPy8V0fJStDEWNF5YLuSJQEhKiqTyxCKYRS+GFE4ECaDpFak2tLaR0ws38f3WHOMLlDMs7ptFo4KejNXeLuLv7KCfYuX2csMnKr0IWlNCV7gwmDtODcfo+3vvdtbBw7hsoD9rtdBoMRN2/c4OEzCyBmn+9dWF9k9+pzDFSD+RPn0bai3Feqh+LwNyP+xgjEwUqJUl41eDwUSbOH3ezr8ddSUrOF1gZjS9Jc85Uv/z5R4KILgzGQTEbMNebACLz6ArrM0abqntLphERnBLUabtDANZrP/4d/y6d++u9y4sQGxpiZq+7q072BUeaanemU3r0h1kg2Ti8QhRKrLUkRs38w4Atf+R537+xXFkbKcvXqJkHo8cDZ05zfmGc4Snn1yg0uPvUo0hTsbm4zt77CEfYNFHlBoXNubF7l7tYthFaV0FQtIi9TSiq/TakNaRyzvLrKQW7wXBfPD2jUA3r9Hnlq2dy6z0G3S7NZ58SZ4wwnEzZWNmY6h+t5eI7FD322hzGlqIOolrRSlEjLoUJgFUIItPLJbCWw41kH3/OIswwSj3RUkGiFlCUusycJaypLp9eXJmFY4+btW0ymMVE9QioXqRSmqGjx08mEVrtDoxYR1QKUoyjKAmurjikrStK8JI4LJqPZ1euyLMP3XSxUP0RhUU5lLZcXGiklvufSarUOl0bZD/VmsJZSa8qypF6vU5QlB70uOi8ppD2SazvSIZ0OOba0zsXzZ5BFn2uX7nBqbY7dg7ssH2vRajZ59eo1xos1HruwgbGW+/eHTMYJZZ6zt7ePNJBmKV7ootDkacERvDqqyzFyiVyXuuvRqXkIpxJiK3KBsZUdlxVUFmUKbKlxlAAMD597nHMnTyFEwV98449BW3IjSa1BHUG24P79ASIQtN2Quttg3J2ixzHJqMvY8RGNFjvb+ySFRx4nZFYwFRFlbAm7E5Zf20amIYk29IYjGo0W97d2+NB7HkM6syfvwAu49dplVh9+B5E0ZFoADo6qZCukBCWrL9haEPJ1JIoA4fywyBNSVaqmmEquQ1uO8nqUpaYsBd/48y/x3Le+iCskp06d4O7mJk5Yp1lrMZ6MCaMGeZ7QaPuUWUaZJaRZSlbECFx8x+fl575JENb59M//Igud2anx8IZrm4R89Bd/kUajyS9+5jP8+z/8fQLPpVWLMMayvbNHpgVBJCnyhIYrCaZ1bm4NeOn6AZPuLTpBwHcvb3Pp3/wWP/u+d/P0ubO4ngdHeBm1trx663lOP3Gapx59L77u8Oj5OwyXBwx6A27dvcVjG+vcOjhgdNDj3r17TEZDHjh3nsWVRXAK1k6usLW1ybHjy1y8eIaDvV1eeP5bjHox73jibbOdQyjG8YieluyUHjrXWGnQwuKYEmN8CisxWFIBjlVIawBBKV1q4ymJFTyzlbD4Z9vc2HO4YhvUjY97OGaYJXzfR5QGQ7U4mUxH7Oxu43kuQRDiHvpyBvV58hL+5Itf5O/+g/+WxeV5onoTS2XwWxSaOM6YxjmD4Yjte7fp7dyf+Ry10GM0meB5AVIIigKyrDJ9dpSDFZbmQos0i+l2D4inE5YWl1FC4buK7c1buG6Ar1x2dnbY2t6sRjmWI1W8nbUzPH5uhcW2z2g65uUXniMKPL779Zh3PvU43/7e97h1Y5N4kjEZRjzy6AZf/OK3OSgDlOOhkylT4aCkRUgHpS3Z+IC5touxsyerRhDwxLkVylSTFDnK9TBSIx1NNk0RhUFoQS4kFnBx8JWHdC2e1+DED7a5+TtfYe0DTyCKSgu/VGOMzRFm9mw16casnlsnwPLic68xHnrMRR6+0WyPe9zODrjZLbA2xbc5RnukylBQsGMj/vAbP+DkiV1acy3u3Nvm2KkTZElBmSvCI4xNokadhXadpp3SNAnWb2KFREkDohKkEkId6ojIHyZkKQVYcbjwFchDn1yERhuDsQ72CLdqluV89l//r2y9/Gylr2QMb3vqnbz/g/P8+1//dXqjKatLy1x89CG+9IUvo7yI9vw8flhj1N0lSxI8J8cQUAtrvPr8N3jhe3/Fr/zmf8R3fajPJgv7xiZvx/D0Y4/goPnYOx8jsjE37t6jP7jPfn/EeFpw/txD2GyfRGvKNGPY3cOgSAvJy7e3ObG8yPLyMm+++DhLcytsdwesW0uZz74IGg2HhK5PfzDAGp/eqEspLF4Y0W5JTj6geGTtBDf39ojzlHw6ZTwaYDHs7dzn7PnTIAxWW+LRiP2tHcLAp+aFTOzs0Lg012gVMIoLMuswHffwfZdaFFUqbKLCnksrUNb+jd6umk0XQC4kqd/g8v0h20NAOIBGydkXuFIJpJGAZK7TZjgcImWFbHAdD0cpdFlSa9TJy5THnnyCWqNOGEX4SpHmSdUuF5oiL8nSlHg8ZtTvMR3PvpAaDqdI5VAU1SisMmaWeJ5LnleY2rm5OdI0JUlSkjjHcVzA4khFPQhpNNosLizSHfaohQ6m0JRljilmT5pvfvo9HNx6hvFBl6TURL7PseU6kZhycOcqdVGw3KlzoCuz4V4vxgB5MULaEBnWcaMaRTrClCWFLYnqPs1Wg6w7mP25SIVISx5cOcG4iLk2HKOsAVGSJTk2M2AsuZRIpSpVSscitKEeLhLVj3H89ArGb+Fkz5KWGdIReJ53pErTrzXobh1gGz6uVGwfdNl1NQv1Bnk6ZTwcMU1K3NAHW6CUS38c02h59KegskoqePPKTY5tLJBMBtQbLXa7A1rl7Mnb92FpbZmDgxs05+doNFYoKHCUwPdfL1YqhctqKmKRshqZKWswWlcSrQiUMghhyNGUVv2N/vb/O/I8487Vlwhk1f0kSc64P6BerxG0mggM6ydXGfV6ZOkE04dWzSMIQyYqpEj7SE9XSqZliR/WwJZsbW6xfuLkzOd4Q5O3sIb/8R/8PUwR06mHPPX4w6yuLbO73+VP/uLbFFoz6u7w5vNLbO8W3NuNmY4GSC/iztYB4zRnkpeca7U4vVCns9hGltmRvSMn4wmeUuzt3GZ35y4HB/vk5eESx3ist5aJ6k1OrixzdWcPH0mz3qbUloO9Lg+cv8Cg16URtREWdrb71KKQ7kGPeDL7JZLkGbHxiQ3k1iFPpgTCpd6ukeQghKleKqtQlkMt5cMMbiE1mkGeEQYO1wcBudeonGvKnEMA7EwhpUCgUNLl+LHjvPTi8yihDiGLCiUctDUIUXDx4dOceegs2XDIxHFwl5fR0wlFCWVuyNOMeNxn2N1iPOySHUGK1Zjq8yopycsC6XkISwUNBRqNBq1mk36/+r+vG3F4nk88nVIWFQ/Ad3zub2+CNviOIgo9lDP7AhevQX885NxSk93BmLlawPpKDYocKyacXmuytlBnqxMyHk8gz9C6QE9SZORRWz1DEAXE+5uk0z5eGCA8B9dVeO7sHZHVhvnWPL3RhChSNIWDUIa80EwnOSbTKCSlK1COrdAKroOLw2qtzXRnyL1dxWayS/hIgpNBUVAZEBzBJEP4HvHWiDKMqIV1xpNNevGQy+k2kS3ITEhcTLESZGEo0ykDA+VUo13DnKqhraI0kh//sfdzsLnPnVe3sK5Dbmf/3dYjh7NPPMrXvvz7TA428eoruDUHaSX1MEBJh6KwCM8nzyvkCUJUsEFT4riSKIwI/IDVlQUsmm5/wO3tfjU/nzF2d3eRZU5uQVPBnLd37rG4OkcjqlMWKVI53LlxFYEhHg7Z9wRhvYnn+9gsI0sV0mj8WgPHcQl8n1defo7F5dWZz/GGJm+sZb7u4Xgh1la43lYYMDaGkytzzJ8/zvHFeXavvUBoBFIpvvPqq0hcFtpLOI4ASro7m3zo6Q+jXM3G+ukjJ++1Y6uIPKMcDynKnEUXTvs5r924hd9YZN7UGEQ+D22c4sGzJyBY4WCaoo1hfm6eKOjQODZPmiYUtsQPPYQUnD97nps3bs98DiUFhYU8qxibUk8wk5Lm2hzCWNIkwSqXUng4VlPVjocJ3CqMq5gUKdfjKbazSr3ZoCUNwc4Qq2ZPElJKLJZzZ88hELiOQppqjSOocPaT6ZBzD60xt9zClgM+949/laWFJS68+Ske+ORHmU4SRuMJN67f4WC/x2AwYjocVNrrM4bvuZSlxugC3/ORToUbN0bjeYoL584iENzf3kFIy9LSCo1mnSRJuLe5yVy7RRgGCOVw5dI1pFJok1NvRjSD2fHEV27fQ+clvcGU9mKHT73rIsmkR57FRPU2AkmaZJw+ucBoEoP1WF6eI817pNaytHoe5flk/epS92p1tCzp9weMx7Nf7sPBkO79Ic/fvsMnP/Ru7l+6xWonQGhDMtaIElxHkpkSiup79nyf92zMszR+jf/hKxN2t4/xkY8J2nGGLSHPKrJbeQSP02uvvMhDpy/yh1+9xO7WlCcfXkdODfsHMfEoYy9PMSKkzFOUaGARhF5JqFM6LUu351PPMz7w0bfj2JhGp+Dhp49xZ/MOK0uz28JFHkCdT/7Ep/nS7/0pv/Vr/xe/9I/+e8498AB3Xn2OsjDUaw3OX7iAE/n4gc/y8iKu4+JLS5kV5EXOrVv3uHv9gLc//SbW5hsMez3SI3QALz3/XcKwfmjEAJPumJ29HVq367zr/W8DYxgcHLC918d3QyajPnmeI5OYPM35uD/PHUpeyEoiMopMYhyXr/3J7xGEDTZ+6udmOscbmrxTq3FsjjQu2lLR24cDtu5c48c/8nam3U02dwb0phla1gjCGpMkYW2+g2M1jdAhT8Z47gL1epNWzcURCqP1kba0SRKzFjU41V5CSklRak68t8bB4Ct8/9JrxHNLrLQb7N65x3zbx59rcPnKa0gB3U6H+eU1GvVG1SYFIdpoijzjuWefZTKZHRrnueCSs1jzKIcxE2GxZYEwBa4QlNM+qtYhh0O98sPPaAErMLZAqpJCT5iqDD9sU+TgBnWEM3uyKstqTKEcieM6uI6DJ7zKbk5K8izD8xRRrY7rhfhewAPtRbylReI8IZkmTCcTxv0+/YNdRv0J6SSnUa9XbLcZI44zgsBDa41UFqiMGKSUrK+vsbjUIckzapHPWqeODGvUooDd3W20KWk0GkS1iDRJf6jv7roucZzhHoGCa5I+NosZlYLW0nE2lhfZMSmpJ6g1WmAtnhI4ZTWamsaWRi0gCD2EihhMhoRRvdKILzXuYfueJhnGHMGlPLfs9nu40mHcT9kfpawvdcjyBEyOpDJENqVBK0melzy4MseG47PV7fJaf51JDL2iRz0RKAW5TSgyw1HwWfEkYTpJKUuYpjkXH3qcwbe+SqQM8/M10l7GII5phYI0h1K5+OQca9e5eOEcX3/xGg89+RCnzp6i2fQZJTW29vaIfJ8ym32cJRAUVmHcFucfvoD83B/xja98lXc//TSekMTxlEajydmNYwRBRUFP05RkNGValkRByObdu/zlX/wlp86eJokfoixyzpxY4+7u7ASq3t4extjKQ9eURF6E5zgEQZ25pk+vN0DgUhQ5e7s9fNcFBEqXjLoDOmdbWFnn+rTA2hyjSywaJTWXX3oR/nNM3qWpbnzXdSlKw8HBAUoaLJpIJGRpl3jQZxhn9NOM3HgUWYkoNV5gmO80aIUOF89tUKvVGB7sslg/TmaKI3nQJdOY1aDB2doiZVkiI4c0atNpztGNX2Yyvkvj1Eli6XDp+89x6qxlOhnguS7DoSHNcqTj4PshJzZOMj8/R73WYmqG5EeYNbdqAeeONzihQ7y79znYqsD/Rpf4QqKKGHSDyoDD/nAUUqW1CuMrVI4XWMZlH5Iapa1XzkJHOEee5wS+T793QBzHlSmGcCgxCJ1jipyoHuKHIY6UeH7A0488ypXxAHDZvXeXTEr6gwHd3pB4muG6Pqtzy9RqtZnPocsKUeP4PqnWkJRoYUEJ6vUWVmvGwyGNmsv6nMew0JhsShJPaDYjorCJ5wTs9XdxXYciN0hVze6PUvH60rDVG5L4PmvOPHe3tmjWAmQJ6pBUFgQleaJQssBRBbXIY3V1hTRcY8QcSoHve4zyDLcs8T3/0HjkCI4tnmXj9AINM0LUu+hcU6tHxLHBFz5SCZRVuEWGNC6pDnmzF5DvHfC1Wynav4jvfQfFGGWbJFlKYQtyfRTrElhbXCPwmyx22uSxYWe3j3Lr7PRvcWFpEWkTak7Bpz7wLv7gT79PXgoW5yIeO7OKBM4/scb8WhvpOBSJIJkKjPXJRj3a/uwIC1e5SAS5dbnw8EM4puSV5y9x6YUX+OQnPolyFJ7nMB6PKDNLmqYEQcBCp4nvBSRJSpZltDsdFheXuHX7Lp1mjVa9heccYTczSsFalHLJTYnjeAjp0V5aZrrfxXMcWnNz2BKyNMZRIafOnOH2zVvs7B+gP3SaydBUkM9xgjYJaIVUDoODg5nP8cYuLKcJVjUpc7i/f0CWJTR9w437B7zPuLzpre/lTW932OpNuHZrk7/6wUu8eM2nFkg8mXCh3eTJBzbYWHLpvvod5k5tEIsEnaY/NAWeJbrDPgfbu1xNkoofay1xWbK3PyKejEhEyLVX7jJKhvjSZ7Rzj2E8xBhLvVHnxPppBJCkE777rb+gFgY06hFFNj5SZbWy1GR6b8wr3/k6/ViDqbQrut0+C+0Ir4zJ4yllbRErKgxrFRKNJHRL5hsOMlbsT6ds9SZE0RqFKCp25owhhMB1XYbDYVX1CovrKDzp0R9PKKxhsb1MuzVHFDbASm5cu8x4cAALCxzEY3Y1THLoDwa02y3m51qsrazRbM7uUagcW+G0pYNyPNZXQ6Tncn9nyOLCGts7W7hpzDseXGHJyfHnFvjOs1s0m23m2y2iWsR4OuDylUsEkYsTWZJphtEa35+98m4sLLP68I9gzJi48wTXtv6KR88uUwvaJIc6I47jcrB7h37hUW8t8aGP/yT7/X1evDNmc1SjU/PYn+4y3bqKEgpPKUaFxY9mN5j98b99jlvDnKcePkYep7y9t8qTTy7yZ3+0z/r8HMY65EXJaKTYaDv87Tcd57VBwO92P8EzcoGi/W3e/qYVgmbGSzeuYVSJFlBXbXxvdpLOKIk4uehwIp4Hpnz5a39Fr5+ztHiCsSw4tjbP6sk2p093OH9xg+8+ews/8tgc3mVnUPAjn3grRdxjdNCguzfCX20yspb1tUWUnB0FNBoNuXPzKucffZCFM+v8vb//C/zr3/pP/C//9P/gRz70CVSpcV2HWlRnPB6jS8Pe7h79wQBdFKSFwSDYOH2C27ev8txzXYwRnHvgUdrzszOjsyyhLCa4jkcY1HFqHp3FOYQpWHnk3STTAa+9+CLtlVWm44S1Y2vc3H6Ns23Fmx7doL8fM2g6xN0J13emvPnhE7ieghKmo9nRWW+sh6W2lKXBVYYo8EjiCZHycFyH7d19zp7eQLgO9QgunD6NMfDM81c5GA5wGz6BcjnYuc9c4HD64sN49YB8OgZciiOgCVypmEwm7OgcgQBd0i0t1nfwgojRoOD25h5RULLQjHBdwfHlZfa6uyTpgINeD+n4eJ6iVgtZmZvnTW9+mK27W9y+d2f278OHV1/b4gMf/y/4gy99i62rL3Ly2BppVjLMCgrhYinR1kVqibWHbVYRYEIHygRBjH9oryT8OolwEXaKOsLCUgjB4sICW1vpoeaMRmNxrEWbEm0N02mMoxyUcrFImusrKJtgXUUcOtzvjTjYn+AFEWEYUm/UabUaRzKY/SFmWwocV9BZCHB9h2ZjEc+1tBs+nYUF6r7E01263QFRrc56vQPWEHgue8MuWRYjBZg0Q+cZxoJ1j0B+kAIZNCjjnDgpaXUC4jSnP8zwayHDUYlGIaXCCp/VE0+xvHaOWqONce4zeWXIxvI8drfDdQRlkVNklRCRc4TxTTmQpDKld7/gS1/4Pp9486OM9/rIIqDWqFU+rrmmlfk8ffEMo1GfL26d43t7hnG5y/lH29TZI437TIsEqw0lBiNcmkfoAC48cgHpJRRlTKsZslAPefTcBbTM2O/eR2PxHM3KXIPQ5ERCY41Dpht0GpJWFDFNYrrjIV7NZTLcZ2GxQWA9jkA4JTeaze0tfvozP81Cvc7xEyd533vewZ9/60W6u9usrq6yt3fAwcHBD0eBg8EAaw2jQY9pVjCcJGhdsLuzzfzcHBsnT3Ns4/SR8ket3kBQ2QPqssTzm9RqDcIwRBV9Rjs3cWVBWRTUaj61KOGRswuMB2NWhaSzsUgyLri7e5fjK3M0mxFZljFJpgT12S/VN7byNlUCN1rTqEXUo4DFxTmefOIxPM8DL8Rgcb0AKRwunD7F2x4/z9e+/xIIyeLqKpGdcn1nkxvdLu9527uohU10LcBms1e8rXqdfq9L5irMIcywbyQ1ZQiVZDedcKzVwdMZ2/tD3ChgqT2PQlJow5Url2k226ysLJAWMRfPnqDZavHM5g/IprOjK6bZhMtXLvPN77zE8TMPk0xTLl16lRMnj+PW1ym1wTMFbrmPjyZUlvnlDCEyvvrdO3gdH+M3SOQOcAJJhGZaTcePgOMVQmBMeTiWqZYwhdYYU+IIMECv1+f21Ru4F86jlEPtzCmiaRdrSmTNoz5xcCUcW1ul3WrTbDSpt5o06q2ZzwGCPM/Jc4tQLmHhEjVcgrkFXGVZaM/Rbs+T0CbMLrM7KnGXTtPwFLnVtBodxqOMVnOBm7duHu5Cqu9CF7N3InOtOru9A4oCiniXdlMxLRU3d4aEkcCvrZEYh6J/jbCzihMcQyhLPaxz9tgq97uwttSm26iDqLQwpFAVW7Scfbw3vldwZ7jPftxl8/qQi79wjs1r+xTWZyEI8FxDqT2+/+xt/uPuhOcvXSY73yRudjh59izt4TNM779KLgbkTo5xLKWxSJmTu7MzX6fpAZkuiEKBMC5vf+IhVldW+fI3/pxTZ5cZTCesn10hLUcokdHwLNY43N3sszJXx1Ewmo6Ym+8wv9BEjQt8oXGNQxjO7tpea8/z2JvewgvPvoIqC778F9/l1q3b1L2Sz/6r/50f/8mfRoR1pJQ8++yzjIYjLJblpSXGwz7nHrzIqbMPcO3GLc6ef4hGo4FyXPqjyZE8cBfml7jrB5S6WqYXRc7O7j6elEhpSSZjiizHaEMQKaKGw8UHjvOHX3uR8o8HyQAAIABJREFUC0+d4eX7mm8+t8+d+0Pe/84LxJOMMs9wXYfAn70ze4OTtyDLSmyZ4/sujzz0ML4sWVpaxheaIi2xViJcF6EkEYK/8+kP8dEfeSd/9q2XeObaDR46vcQ73/puPv/HX+GP/vmv8ba3vYW/8xM/RhTN/hLUGw3ybIJTSqTrkBiD9hsk29t85Ngyg4UIhCby2nz35i36w4Tb3QGtVot2q4VsTakHiknvgGguoOla/KxEuB6uP/uisNmJ+KWffx+6EFzfTvj2yw00DVIr+bMvfZVHzpziyUdWeecDFt/s0fYzoqBNnvi86f1r/PrvXeLPvnePh976OI67imsjpJYU0sUewcXeGMPBfjVrM9qiLZSlQeqSQkO93WR+ZYmo0eJgr8fOnW1eevY5WgGcXGpzRmYcnw9YPP5WTpx5EM/zKvZo6ON5s5dWQlikgmNLDQJfIiYjztomkSg49/gm8dihLC/z+OOPsbu5zeJIoOoJyShj99WUO3d7fHF7Dysc8uxQ4IwKi6uPsBN5+PyjzB/PkVnKX33tcywsP86VwXlqp9eYa9XIS4/BsM/W/le5sBhyfzhhkYIwDJiMEjbWlzHGUmiNtRV80XMdQJLns18i10db1HsNHjt/kkdPLNHPYtpnWzwaJIR9l6m2yEaNe6Meu0Zx/n2PE7g3mCRdNr/xb3ni/DrXypRCuIhigvAkUkiQmjzJZj5Hp9lnoXOcNBRcenmPa/cu0+vvU6+tcPJYm9W1B6jVa7x29RKeB426x5ueOkWn0+HKlVdZPj5PbbFNa24dVU5pdlosLZ/i3s1vY2dfzXBnlKDcOnksmE4LJusXqYdtJjeu8fm/+Drfe+0WT7/j3fzsz/4s5x97gs9+9tfo9Xr88i//Mj/5ns/wzW9/n+40xe0s0zUh/ZFGSvtDoalZ49jGGV55YZ5ksgd2SpyW3N0csLV5nc29bebqDRw/oFmrcWKuzpncUnuh5GP9E0TPufyfL7yKqXl8+iNP0u9PkRikFExjw6MPPjnzOd5YbRMsZVniuS6ltaAtAoUUGuuHFTUcKrqqraBqVljaNZePvP0h/sVLr9Drp+i85NOf/BBf/dYlXn75GsnHKv2KWcNRDhunT5BOx0zjGL/I0emAOUfj1gS1IELIajxwttlgPy24N0oY9PYYjPqcWD9OzXeR2uI5LfpbPW6mz9NanGPBPzbzOaSw1PySaZ7RrvssL85z6epr1NrzrK6scufuNrfv3Ocf/Xcf5dzGBun0Oo2my0BPaEcuP/tTZ/nWC3X2JxHCiyiNwiqBNa+D/GaLoihwlUTrKuFVAlUAAuk4WCRpntEdDGg0GtRqNTrLyySjMde6CSe1SytUqOVFluYX8XwP5Thkpvyh4tss0ax5GBQYSZpY7pqCWMY4vSHPfUNhjSVLSy5cEHz/B11e2QvZ7t5nOs0pSknTK+n3E4IoIPA9pIKiSLCOg3eEMcH8/CrdZJMk1pRJzCiJ6E4jUmE5GGxRDzuVbIF0CKIKmnjrzpBOPaDINQ+eXqPfnRAFFRlGCCrKupAUxewVXtjxccYui3MRrioRKuS1a7chHRJPA+KwSW8n5Km3XmQpKvEMJMmIbWeC0Hd48IE3sz25wThTOLmgLAVGViQTdQRc89rcPEpJUmHZ3NrHKsU40ezsToheG7LWbtGU0CkUUSlptRrUIsWbnjrDna2rWAntZgNPgXICpKyRGQGBodSzyyfsDCeECrSwaMfh+JveQjHo88ATb2Pjzh2y6RTCOi9cuU6tVuPYmfOsnjJ8/fvPsbm7x+b2LmGjxerGObQ51M0+lHc9irag67ooGQIOWTYirHsICQbB7n4XJV3csuRkVAckWhTUznaYDOH6eMzaqSWWV5fJJn2wpoJuGkujtcbC6vrM53hj0SYVnIC8AKktrttAWw22qvYwGkfIiqxhLY5S+H6IlIKljsuTj5yn4QuiMGBpeZ6f+/RHuPraHbQtUeIIwzNh6HTmKBt1gumE3t071IuE+nKbaTwhjQu0rlxbNhbbzCUprtHsTA37WU4UOKw2I3qZIVWKp55+muXlBYbGIo4igFSWuMoQemB1wt07txkNh5w6/yDdvV2OnzrJC5eu8C9/5XN89P1P8LEfe5z1t0bMHdzhygtbrMt1/ucPfopnXrnHb/9RRukKtBNDVuMIHAyMNRSmWuZYqudgEeC4yKIShcrTgng8pixKxuMppesQdDo4jmRze8yp5RpNZWk1W3hBiOv7TNIJ2RGYr67rgNA4XonRimFakGkNk4L6ROJ6Dq7VvPTsfaZDSz/NmY4nTJOcWFumqcGVElNacKsfWasVMU5ikvHslWbgKpbaTfrjhNCtkeqIvHCJywIcgSoydF4iVIDvKlRY487mAfd9j5qyvP2x0xyfq9PrXuTP/tit3mddgnIQR+iIllohLwwOqNcuIpTHtWtbKOp0dycIFKNYkpZDjs/nqKSgFNCdTugcm+PJtz7K8okO6902V26PcHNFmRtKUWJMxdqdNeQ4YlwMGSLZ62coUVbyzIUlGYe89Nx1vNMnefPpc9y4OmIgY7I8YePkPEuLbQYHfVpNcAyoVptpnrC9eRlH50RHMKfoJwWx1JSOSyEgjQt869EM65x5ch1fCYIiYTCZMMwnPPa2dyGE4Nq1a2x2J5x48FGE8kilQ1rqQxr969r5s4eSDrXWEpPRHq4KAIk1Bguk6Yi7mzHWQs0NiK3gjpD091xqfsk3d0cc32iRFSlCqor8BkgUa6cfxD8CH+ENTd5ZlgMCKQ2OqrR4i6LECoHRFSMqCquXvTJeEHhuiKMcdFHwM5/8IEpUkoylLhE65uLpNXppQhLPvrV2XclkOiEKKspy++IFsjQhHg1x9gzNsKpSeoMeC/NN5k2Dlbkmo7TgYBhzIB2U41AOuuzducf//buf4/jxU+zv7DLsdfmZz/zCTOfIe1NcI0hzhzgt+Pmf+THGScy/+50vMMx8Cutx9sHHaPkRt8ch/80/+WP2etv86Icf4cf/1gfo3b3Gd3/rs7zlnQ/wT/7xk/y733mFW/ccChMcKUlYIC8KrJHV6EK+LvgjEKbAmIo5ubfbx4g+QgrOnT9Bp92i3qixsfoALz77HPzV13nnh38K5XpI5VCPJ0yT2SuraVqw2InY3ulTGvC9gEymaBKGY4ciL/GjkD/64je5rT2mWnJiyccvJHqQ0apFRA3NaJRSlCkmLgCXdtNlvjP7q/7Bt5xinK1x65ZDa+5HCb2I9z61hgxCLl25f9hV5LiTs2RGsXvzCge9Ef1pTmdunq98X7G/u8e1q69w7OQZAs/j3KkTPPrIozz08CMzn+NLX/4Bv/Rf/iL10OfOdp/rL91kcW2BYH2RemceudsjKlzu3ouZFgXFSOOUFrE34VPve4Jde4PGsYLFrs+BDohHY3RZ4kqFd4QZ70rYYLuAl+/eRPklrvV4y6MXQE/58l8+zxXl8L3Lt/jRt5xi5GjmFpuM4jF/8CdfwQ2b3Nscok422du7S7SYojwPoTRSQ5LOPs4aluAJUFbjCEXdBCA0mS1JJ0OkUPjCw/E7aG0Yjiv9bn/lNPXA46AwFCkUpBS2wtwLC/IoqnZAvV7nnR/8ONdePcXVl79DNrmHNlVOK4sUXSoQEscJsDqn8HyeuXkfIQxeRzHqx1h0xQ8pDVmmqTXnOX32IZwjSBe/wWgTfUhp1sjDCltKB6yhLDVh4FMWxQ8p4EZbjLYIqcjLHM+rJDVLDX4YkiYaa12S6QBxBGlJ5chDhGAl8qSNRihFWK8hzBy9bh/QNOs1iiJHGIvnSALfp+659AYlWwd9+qMxdSG5fvUK169cA2k5QiGBSUtKqRjHmklsKLKETjPkEx/9UX7lN/6I+/d3ObG8hDy5zN5wlzCYo3e/y7/51e/z+f9wmX/2zz7MODvFH/zekM/8/T3+q7/1Nj73O5d45Z5FHqGaqDqdigQB9nCBaSoFvLIERyCtrUSVHEGj2WB+oUrcYeizsLrERz/5Kb78hT8hDHxQLkhJ4AZHmiXqwnLQG5HmOUp5aGPQpjKhKLKc4ShGTQq+DoSRol5X7PUKlBJEUcg4zdClptGqujVjKzVBYywUR6g0HYUvfYwQBF7E+vo82pP4jXne9a55At/BdVz+/A9uUK8FnHv4YV548SVOexFu4JNMxviO5NyZM7zv3W8GI3jg1DnW1pbodGZf4JbxIkMzptUOiFhibvM12nOKrYMDcmHp90c44SI4EAQurpYEkUMtELTqHe5efoWojGg4Hl3loZSLbwyecqi7s8sFXHrlZUStQc2vc+HiSbZu9kizEVEkOLZ+jN2DPseWlpHGpe5IprEFP6DXs2hSonrIQa+LtBmhtUzjCXk6Zr1TQ9vZiy5T5BgFqpLdRNlKkEtKiTUlVpdo5VLokrIs8ZXCYnEch+TQ01IjScuKPyCUQArQaX6ksYlyPWpBjTNnHwRpePG7BzjKIszrM/RqcpAXGY4bVL+q0lDqDGssYW2ONI6RysUYS621yMUn3oHnO0fqVN9gGzQFtmKECSXJsxRHKdxDCFWa57iOAKNJ0wxHORRFJU6EfN0GykGbgjgtSNLKVFQIRXkEenylOV2S5xqjIVCVjofGErYbtCRMBmPMuKiIMEIgpY+QJY6riIYDtFejtRih723jH/poRq6szCNmjCIzELjEuaA3SrCeIk6m6LLkPe//IC8++wy3rt9knO7j1zwePvMkF87k7O179Ccj/uE//AIrKx3mmovwG9/l/e+N+fC7z3L1t7cPE/FsYXUJUlakHCrKtbaaUmusLvCswhiL60C9EbGw2KbdaRCGPn7gIVTJ8soxPvDRjzMcj1lYXEZYQekoPDs79CkrS/qDKUKAcnKKIq8u9TDE9yst9azIkY4lj2OmOsPUAhxXEkbguQ5WeYeLT4kwhizLmU6oRikzxr3dA9pLq1y7sYeratTbdcJWjcRartw4oNMKefTiBidPnqfVafPA+Yc4vrxIo7NAFIX0DrpEgYN0XBYW69y4freSeFhcxPdm382cP/8OInziMuOl+7d56twGi53jnKy7fPPlHzCMfRK5RWtOEffB9QQLnQUckeF7ddpzx8AE3Lp7H7RlZW4Oo3PG05TwCBorN7v7rHmSyG9hZYYQklwXFBn4dclDyyfZWF4mjw8IwhrH2g2mdkCSlTieZDyd4NegHirSNCUuUhyhQUs4wpgxet3HVFty7OusLqR0EGUl2WzRWCEobIHIM7CWPAPrOAgt0AasUBipKtcmXeDboxVdruuSCUFYCzlz7hE2b1xluH8PqQy6jNH6EPIKSCEpyvwQimiwRpOklbuUMYba4gbnLz5Ju7OM4ypazdkv9zc0ecdZRuiHRGEdo3OyLAXfR8jKVBUpkZ6HMhZsjpAGx3UrzKbWFMbgeR7/L3vvGaNnlt13/u598ptD5WKRRRZzN9ns5sx0T3dPnpZmNEmyF54VjF17jQ3yWoK1WMC7MCAtVhB24XWQtYK8trTQCHLQQGGiNJrU3TPqMB3ZbLKZU7FY+a03hyffux+eIme8X/zWl8YY6AMUwC8kTz3v+5x77jn/UCyWGA6HoGEwaIOWxOn4Tz8YBaRC49o2WsUkZFKsluuQCo03YaEdG7tcYLC1xbAzQto2bj7rVj54yGZ7EHGv2SEtKw47FRwzs+zaCx385ZsdTizUWesE+Ei2uwHDWOEnUKgXeOpTn8Qm4bWX3mD13hrrmy/w2JEDnJhZZOLQMTpJns5Om5Xlu/zeH73Fv/nyFWp5i5/5/C8QqPHzkNLY9Z/MzKAtYXD64ZNEQcCtG1cy5IoQLB06xOR0ndpUlZm5OqYlMU0DaSoGfo8DB/fxza9/naee/BAnTpxkT9J1gDQ0hYKDZRq4jkOQJBiGxDAynXHTTHEcQRAkGDmBZZtUawWkIRj1A4I0JAo0UaSy7t000KliopbHcccvEocPHuT28haf+blPU8ylXL18i5wqcmh2munqJKNEceNOm9WgypuXB7yxepmcY9LqrLPV7GNJyeGFGo+fXODAvjJHFvehpZkZD+zhkdRKAf/mX/6QfM3nxOQEX/VLtMNbPHr4QCZf0Ep46fwFjj9ykGLeYXaySL1aZnF2gjR2OHj4ELeXb1GdtaiX93H2oWO8c+kyz71z+z8yI/hPxexUiUMzE2yPQvJz8+xs3CA2BYWcw+KiTS8WbPZ9CoUiI6UxRIdizkAPQibLJaSjwYCc7WLbIYHfYthIGRVtgmj8G3MUhZi7/pSkCVqaJEmKJTXD0SgbVRhmdohrQV/J3VGtJiLEAESqCKMQkQRUCjnKhRLFcm1P8hq25VKp2gT+ANeJ+Oinfp4oimlsrXPp/IsI3ycc9pAqIY5Hu0tRgZQ2SZri2Hkqs7McPHKSEycfRkUx0jTJl0qY5k8pztu0LbSAJM0ICxoNQu4agu4uD4Qg8H0C30caGssySVNFEscYhkEYhhiGkblZqEx0XSvFHkANqDTBNrKTV+pMwN6x7ewDlDozQVaKTX/IKE4zIwASIDNQtgyDhWqR2WIef5QjTSOQkvrMfvbiydHuKHZyMUM/RUuTKE2JU4XGyAxMEQRhyOLhQ0zMzLC9fQ/b88DIIJeG7TA9M8/E5CwrTsq97QZCBQyDBLEHRqEhrUx6lqy2zM3N02y32dnewrVsUglWLk+1WqNWq1GpVMjn87vUc0iSiJE/xJRdTj10ilazRbQrFrYXwTCVaNAK3981MZaZ6L1lZr6MWotdLHqmFaJS6HZGOI5FmmiSNME0LdIECjkPpWNSodFK7Mkq5catZaYn6xSLNlJoKqUKpVKZURiQJpp6zqNycJLrd7cIRiO6/RFhHBGnCqUzx6FLt3yG3TaLcy6uZaCVQZpogmHE9OR4eVzeuMrsgsdT7zvNznaHG+ebrHVabK1ucfhInauXmzQ7LcJwjqUT0xw6PklZlBE1uHurQXlkYiR1XG0Q2ia3N7sk0sG185jm+J33ieNLyEhS81xM4bLvwIgoCJibrhEEQzo7fVIlkFaBqUqN0WAbN2dRrpTwBPiqj7RsQGM7BqgYHcPQH5LsYXGqUUSRT6QyKVWvWME0JZqUBDCkQZSmCCEJgojGYESSZoUhFYq8Z1PN5fBMmClXmalUsKVJz4d0D/XjvueuaVoYhoEbuRhIZqZmKX/sM4RBRLfVZvXudeIoAiGIQx/TEHiFIgePnGZqappyuYRtOyQ68yt17BymNX5JFntxTX4v3ov34r14L346Ym9r1vfivXgv3ov34qci3ive78V78V68F/8ZxnvF+714L96L9+I/w3hXF5b/26/+Ta2dIqZI0SplGJtYUlPxDIr5HHGSMAojdgJQhoNlmli2TZpqpNZMeZK5SpFKzqYfa9p+SqDg5KGDTE9PceLxT4+1/Xjuue9px3EzDQ7LolAoIIUgSRK6nRYr91a4des2xUKFhx56iBMnTzI5NfVgsXpfMyMjs2Rxf3ewa3w6Vh4/86lJHceQJjpbvOoMIypUxtj6SbSGQIAA0xQPCDiWmUETZcYJQAEqTYmihDSRvPBSa6w8trbb+urVq6ysrPD000+TRBH/9P/6J/zwue/zwSef5smnnuYTn/wEL734Ev/Hb/4GjcYWE/UJfvmXf4XPfP7zSCu3+7uLBz+Q4W9BMD9fGyuP/+XX/5GOo4ShH+IHA06cOoxjujiuzfGTx7AcAULx2ImncY08Wqf88R9/ib/4y2eZnJ1lrXkDkDQbXQaNIZJMq9wwTQzD5s3XXh0rj9//17+ln3/+eWzb4ZM/82kcN8fOzg6lcplXX3yVAwf2cfr0Qxlud3dTvr6+Tr/XQehMya7dbrO1vc2V69fwPA/TNGm0BiiluXF9eaw83n7rJb2zs4VleWxtblMslWju7GR666aB53k8evZ9/Icvf400TVk6fIC/8QtfQOvMGV2KlBdfeJH/51/9PkeOHuZTn3qS577zPT714adwHMFDT//tsfI4ffYR7QKlnMtjix6PnFwiN3OUJ372i1x/822KpRxRMuI7X/8jlmZz5CxNwUmwbQfT9LAdj1RpbNvENhUJBlpaaLPCcBTy4f/y18bdWv7YxVXf5yMkbDWWcV2PGzdXaHYafORDn2Kju06hVMeQBnEQEUcxhUIBzzAxDQNDyAedq9KKUeRTcPLjfS5f+13tul6G0jIz6zkhDOIw4bXXL7K2tkG3283ggUIhDThx/ARLS0vs2zdPseBiGAZRFGFKg3gXETMcDtFoDn/iF8fK410t3lvbDWy3T8Ez8TyXolslTROCICRvGRRzLtVCjvwooD8K6QcjRoGBYTkYhk0nAtEJiFODfjAEaZLP5WC0w2hnfPqzNMQDeKneJXJYloVhmMzMzDI1NcXxYyd4883XeOVHP+TVV/6a/+rv/vfMzMz8mGQks2L1kwvfvQHjwDRNdMY3yJw50jTzIVQyw8ILgf4J30ohRPal203elgaua4NQ+JG/q0tyP5O9ZWMYRkYuUJlnpD/o0203eevNN8h5Hk8+8QGuXn6HMAixTIfBaMjlSxc49fBDLB77MWvwfuH+/z+bcWJ7u0GtVkAkEZYW9Ha66KSLMECTMLs4geUJWp0NqqUZHNMh8lOm6/MUc2VW1jSRHxH1QjzHJoljFhb2sbXZIO+OL1x269Yter0e+XyeSqXKYBhw8+Zt0jRlOPB55+JlavUa1WoB08w+j263SxyFtHYajEYjlFJUq1WklLuEpwSlUnx/fDW/XC7HsWPHUUpw4sTDGbEMkEIgReZ6tL3dYDAYopSi3WrvPv8MtYPWXLl8k1arx/bmNl/9yteYrk9RmZpBpXtgJAtN2dKc2D/J0pxGK8W++X00NlYplUq4OQsj0pw5+z4GjWUiYpI0xkhihHCQOmMfohNCmblogcJJB5Scvb41WQgh6PcHeJ6D7UjeeustpF3g4dOnUVJgeyWGQYrtWDheAcuDVCma7TaOaVKr/tgEIooiLl27wuOn3zfe87A9TMvKYKyWgTBd+gOfq1dvcuHKDQJ/11dXKYTQCKE4d+Eyd9e2OXH8KM984mkApGFlvArLRO5CdffyzryrxdvLFwh6LbZ9A2n5HChHuF6e2HToD3oEwz4FL8dsrcJs0aMda26v7+APQ3wkcbFKEsWMwiG2nZJGIyZtA08a9JtrY+dhGRamaWEaAkNCs9XglVdex/dDhsOQJE2oVio8evYUp888xvrqCn/xza8xNzfL0WPHOXz4KFqr7OQ0M2eV+832npha0kTJDKKXCvWgmzBFxo/Uu+JQaZIidt3cdapRWiGFxDItDGlm8EYRYRsSUyqCNMpOhL3kYhgPoH3NnQahP8I0oLmzxfraPS5evMBrr76CYzsYlk1v2Ob8uTc5fvgoi8dOZ8XiJwr2j6Gf45NS+q0+hZyH0ILIj2isNjClZBiMsD2b2lSFVrfHS1svUC1MMFGZ4+7dLR498xgz83O88MqzgAQJhWqRJI6ZmKqzvrFFGI9/uK+trWFZFosHD3LkyFHOvXWR7e0d0jTFkS5RlHDh7Yucfd9p8nkXrTX5fJ7avjlMKXjjzTexTJNcPk+9Xt+9VSlEx/8Jl/P/dFy4cAHDgMZ2i3Klxuc//3lcz8t0Z4QgjiKu3bhBEmdU68FgCDpFpwlCaDrdHleu3CQKNFcvXeWZZ57gM5/9PFvdHRA2h8fMw0hjjuyvcXQuj2t08Ao1isUa5y68zdLiMWzhEqXgFeqE/og0DomiCK1SHFOSqBRbZAbXgTIzXgdg6GTPDQ9kJLvhaMRrr73GkSNHGPgbpErx0Mnj5Ipu1jgIk9XNLfK5PFJrTMPEdR2CTpdqsUAmk5e9tbfv3OHZ558bu3gLMzMJkWZ2u2x3Brx1/jLXbtzBj5IM4pukxFGU1QYpkHHMKGzQH/q87+wpypUyrucR+z62nVn/ua5LtAdd8Xe1eC8tzNLs5jGVIkXQDVNU1ydSXWQa4lgmfT8gNU081yVnwJn9NSzLRgnJTmdId9Bl6Kf04xzStIl7ETYKtYciASLrXqTEMAyq1SpPPfUkb799iVdf/TbNVgsB3Fq+w/EjS3zqmU9w+Mix3e6myZe+9Ae4rsuxY8c5c+bRbNyBzujYGJhjAv4NYSGtzEPSkAJDZKMYFWfF2TAyA17Lch4I3yiVZozTXZF/KQXDwMeUAsMQmMJEmhDLPXglKo1tWsRRRBiEfO3P/5RLF94iiiMilXDhnXe4+Ou/RiFfoDxRJ041pgX9fo8f/uD7/Pwv/m1iBTqFYDQgCAOCIGD13irdXpdf+qX/dqw8RqMRq2trBP4IQ0jybp79+w7g5ou8+qNznLt4nsnpOh989ENs3V7h2qVnmZ6ts7J2l0grHjl7llfeeBmv7NDpdgmHMRsbTYQwSUbj045fe/MyZ8++j0ptH9Jw+NKX/oBKpZLRrGMfx3XYaLS4tbyOYWjazS0cM8IfTuIW8pSqJVqtFjfeucXPPPNzvPrqG/S6Q7QSJHvQ8/7Uz32BS5eu8NhjM6RRjOsVUEJnmkBBxPe+8z3efvsCSRzhejmSSPB7/+qPSJKYJEnY3Nyi2egjpU2ulONzv/A3KJVrfOk/fIUwiPngU58dK4/DkzXef7iEK7eJDIfjj3yYt87fpN3tcCG5xuXL12i1+jxy8gSLC4eZmqzhr7yJ390CmcPSCq00roZcnKKSMOvEzYyFOm5sbWxSn51iZfM2z/31s6w3Gzz/+rMcPXKUz33u85imhVLZeDHn5pmq1xgNY4I0gTQm0mAXSpDPsbx8hx+9+QpT09P8zu/832ysrfOP/+E/GisPw3GwvTymKen3fH7/D/6EMEqIlc4crwxBrBKCJEKlGkMaFItFXM/DMB3+5M+/Rb6QZ2pqis888zF0GiClAp1g7YEs9K4Wb1dkfotpLEgTTYoCCaa0M1JHqAj8HkM/IJ/PUS8XmZueQKKirZ6zAAAgAElEQVQwlebIbJlE5WgPAm5ujgjihJCU5R2xx+L94/ny/R/bdti/fz9/64tfJAgCfvCDH3D50lWuXHyHJx9/nEI5D1pTKBY5deoUGxsbhGGYmfQaJprsWhYlMQVvfGUwpRTozKJS6EwKV6J3C7TGFJnO9X3tHL37e2qdkiQw8n1inVLIeSSJ3jVUTtgDGxx2O36tNCpVnDl1mjdfe4lWrwuJJlGKcrGMm8shTJMgGJJzHGzDIQh91u4ts9ns0uv12FxboT8cMhwOd+d+0djFO0lTpqo1TEuQc3J8/MPP8OGPfIw4ifnX/+/v0ew3mCzVGLSHXL90i831TU4/coR7mw1u3byFadhoBP3ekMRP0GlGpihXCqR7GFdkt6qAzc111tfXqFarmKZJmqYoEpLUQAqL9fUmpp0iUWxtrHN7+R5eoUyn08H3Q+b37Wd2bo6lpcP0ewOsNZudZnPsPPL5CqdOPYJjOUgEmvusNM3K3RWWb90hiWKKxSKmaZMkKdvbTeI4IgxDfN/fVVYsEaY+O602E9UpmjttkmT8Du/0fB4zzTiK3VByffkakTC4vb7O28+/QreTWQR2Wm3CDzzKI4Uy+fkzxO4WYdBmsuaQBj1SI0UkEakw0FJlM+c9kGN+8Nxz/PwX/wsuXLjI7dt30ZbBEx94nIX5+WxHdP/zU9mtTwrwRwOkZYHImqNKpUx7Z5s//bf/lis3LlOpVdnc2trTuMIwUgwpiUPFa2++zSCIHtxatQAhM3nlOE4y4Ssy6QbPdbAsCz9MCaMB7c6QhfkZDi/O4bjZHmMvzNd3tXhPlx20kTKKNKnKnm4UxuhUk8rs5dDSINYGrd6I/sCn1+1SynsUcwXSxMHzTCZyJoUDdTq+T3swoukbD4raOBEnCbbj7EqgZuOIVmuHdrvNs8//kF/4ws9zeGmJ9fV1gmFMu9VhYqr6wJ/SqjoUigUK+SLr65tcvXqVIEnYtzBPIV/k2NKhsfLQWmfaDLvjBkNKtMpEpQzYXV4qpLSysclP/D2tBVGiMAwDrQVSGkh4IIqj9+CleV88SilFksbkC/kHs2vbNMnnXEwp6Pe6IAy6/QHKhlyliFYxP/jB81xfXiUMQ1QUoHeXllnBG3+2KgyBIiVVGtN2+dCHP8707By2Y/HL/+BXeOOtVwk7Ha5fvUO72SAYDSgV8yy6eUZ+ilM0+cEPnydJVKaZYSgsQ/CJTz7B3Vt3xs4jX8gxPz9LvV5DqZR8Pk8cx7ss32zwrLVkOIrJSUkYBBSKVeI0wQ9TNCa5fJlHHjmN6zqUSkVyuQLd0YBevz92HqCxbDtTv5M/3iVIYXD18mV6vR6ObWNIQRwGJKli2O8/sAC7f0vL5TwKVo5Ou8egP6RcrjAcjm+4W7ATgljiJxa+WaA7ijFzBW6t3GNzs4HneQipWV3f4K/+qsHy8j1+9Vf/JyZmFlm5cRkn71CZthl2txl1VxG2RsuIKInRjH8T+d63v8NjH3g//d4QlcLjTz7BY4+e2XVf323INFhSILSmUihgS5uU7J0f+SOkabC1s8NLr7xMqhMazZ09j26ENElSzcVLlzl//m3gx+9Qmip0mpk7hGGIKQ0cOxuV/aTVmud5oOCll18miR7h5PETuIZHon9KxyY1L+FQYYbNtQ7t1XUcYcFEFVWv0Es0w1iz3gx54Uc/wrIk3aHP2aPzVAoRsqBoXW2Tdywcy6Za8rBsyOdM3ndwek8CN5cuvEwhX8CxLcIkZf/Bk7Q7fZDw8U88TTmfY7R+lzAcYbs2/+Sf/3Mq1QqnHn6E+fkFvvLnf0YUx0RhhO9ni0KNxLTgg088wW//9r8YKw+VZFRelaqMpr+LZJHCBAG2Y4OQ2KZD6PuZVRkK25DoFAzXyRAmOivi6MyCwTAN9B5a753mFhubqwyGXc699RpXz58jSRJyXgnHs6iUCiRJwvLaXZIkU4R0CjZGwcLC5vqNK0gnT7HoIZSDkNnXKtuojz9rJlbIQHB03wLl8jSmaRPFEUJIJuszfOypT9DY2sAQFmkY0q8VeP6vf8ChQ4skQUI+WeCZj3+cF1//Id3tEZVyHtMU/OmXv4W7B7PEp59+giQNkYbCcWwcx+Fzn/scL730EmurTaIoJU1jHj11hlwpj2loDsxXWTx4kIX9h/irb/8VAsEzn/wYv/e7v4U/8ul2B8zOznL27NnxnwcZ5fs+LGLt3ipJHNLptLj8ziWm6hMYUnJvfY12uwcIwjh8gFzqdrskSYIQgvZ2nxs37lIvTRNHcOL4w2PnIIwUph6j14dGN+Kb/+67hCoFI1v0A7sKoZJYw1sX3+F//JVf4e/+vb/HyZMnqRbLDDtdepGkVCrgehZDf4utzTtMT9bHzqPTbPGb//tvsP/oQZ5++qNMzM6wtb3N1EQNKTO02Llz53nlR29Sm6ixb99+CrkySIE0DUqlMusba2ztbBMmMUrFpGmazZn3wI/X0uKr3/wWFy++Q5Jowih9gLJyHIc4yWSU4zjGzTu7N6NsN3b/8xgMBiilGAxTvvPci9y+tcXf/PSTSD3++/KuFu9OOEDc9fne9VUsP+Vx4WJv+ehyH7NWwjIkL755gcpsjZoDM2EF+8Iqd0wbTh7h2IFj9DsrDOIeqjHAME1ankvkZ4bG40beUqRJQLVeQpsuuXye23dWGA6HfOD9j2IbJhaZWHucJNi2zaAfcOPGMjdurCCwiMIQpcB1XcIwZDj0yUuHixfOj52H0CLzWdTZojOTZZXEu05C2siQMCPfxzEgikMqJZvpiSm63ZB7rfauaa+JFJAohdI/NrIYN77znW/TaGwDcOfObfRogGlaaC1wLBtjd54vlMIUAsuUCJWg04j52SlE3iNGZkp+ZLeJ+7Kycg+H6oee+gCBP8SWJrZp0e7uIERCYLmYhk0YRIDJ4sEj9DsDttfX+evXX2J9c519c/PEq02KVQ8VxJTyLqGvGKVgW9aeNJtffumHzM3NUcg5rK0u47oO+w/s58rVa6yutHbhoAlhMMC0JW4ph8Cg2+kThLdIYs1wMODypauAIIwigsAn0gnnzp0bO4/7ock67tWVFXq9Dv1BD384pKMknW6H23du0e+PUAoqE7WsiMYx7XY7Qy9JSc7L02p2eP65HzIcjLh1c3ns/7/dHfL6tdfZ7KT0+iH1mTrEfrZoJ0NFWZZFFCbYZAe2ViP+/Ct/RsSn+egHnsBPehRqRYJ2wKDts9losn/hEPWJ8V3b+0lAMugxGgVUaxOkgC3NbKyoBRgmjZ0ma/eWOfvYw5w5cwYnSukPOvhhRKc34NrtG5y/8FYmA600URBgiL0IKEOsNLfvLJPsWgbe/67fP8gMw3hQyD0vgyRLKR8gsB5IXitFlCZobXDv3jqt5l0MMaI0Zh7v7tjE8Hjl2ZeJ9y+Q2zfB4PI9DuQ9et0O8d2USKfMbDRJvYPM5XJ4U5rR2wavtweElSYTk7PMLj5Ef7BJsrNOmsakg4g0inCt8YtVszXg8Y98iv379lMsFYkTTX1imjRJqE/VaWy3MC0Xouw6Njk5zdrqOt70HBubW0jLAK0YjYbk8152pQ4jRM6m1x3/OnpfiMuQ8sHIQymFFvKBZZxSATYJpWKOYq3IVM1jft88F66vo5sKyORPVZqg02wQ5FhWppM+Zpw79wYgmJ6eIk4ihr0ug+EwE9+REpRm0O8hEdiORalYQAc9hM6KuWFbqCRbtgolHoxNsi5jD7ZfOcknPvIMObfIG+cuMui3SaMAw7DI54oEQUgcJ9hODsf1CKOY4cgnGnQJkhCHAt1eDpVoHAMCUlIhqZUrRHtYWIok4LFTJyiVS1y/eoFHHnmI/Qv7mZ6ZQ+vLmJaJNAzurdzGtg2OHTtGMlFma7NBt7PMxuYmrVaLa5cu0W6t7s4/I4RICYLxDaof5EOGjVhcOsSVCxdobGzSbXe4dukqnW6Xdq+LFJJ8vohE4w+H+H6AgcSQBoZpUKvXaTY76DhBCTNDpowZ2+2QxsBGOgUKhsXDD50gjkPu3l2mkwyIomyfkPNyCJ0JxiEkaxvrfP/Z73Lm+BK93g5522Y4HOK5Hl5xiurkAbqDAXNj5tEPArQhSbQiSVOkMtEqc+jq9fukWnJw6RBvvfY6a2ubrKx/H0+D6xg4nkdtYoYD+xZ4643XieMIQ2uE0iD1ngyIfT+k2+sTpypzkd9tlNI03QUWZAXay+Uwdwv6T3If7hfxOI6JkxR0yoARK6tXmKz+lM68a8Up2tN1vvB3vsjU0QW+/Jn/BjuOSHXIFIop1+b45ATSMbG0h06nCd7v8f5em1ajx/K3v8eGISmcWKLw8EEGnTXyRkwzNQj2cO2R0uThx96PY9kEQcjG+l0WF/ZjIAnCPkEQ8siH389ffutZzNRgu38dKQ1unT/HzMIh7m2sEKQ+ZpIyGglMkWI7mR+nZY//8NPdnPVuZ2tZVlb0VIojAioFh0opz8xEGcOQuK5HlDqIxGduKsf1e5mcq2mC47pIMgMCrdSDf3ucKBVLJEIRS4XtmHz/7XfwvBxROqLkWlTKeVRoYdRn6PtDojCiXqiQxpK7d9Y4feAI8ShFCBslovunElGSsAdjEDpBg1fOv0SYxGyvdvAHPtP7JkmVZmp6OvM/dXMs311l5d4aW80WSDITDc9A6JC17QYpBsM0Yu5EnVjFDO4l+P74RfNnnvlZojBm0BtiOjaHlg7SH/Sp1mp88pmPsb6+zo0bN4gin1yuxNbWBr4/pNvt02p2GAwGWXdOwt3lW9i2jdaafYszCDn+TPNB7H6lvHwOvzdg8+4aGyv3aLfbBFFEuVyhUCgQRxErt28ihIltOViWh5dzKJQLJPGIOMq8NJ98/DEOLS2O/d8vnPgQd241mN83wwfOPsSB+QXu3V1j9WZGQIrjmOFwyMj3kdLAzReI4xhblrj1Tpvf/PV/wdFDcwwHO3z2s5/m4ZNnubeyRqtnYcoxJRaBkydO8ZGf/SRuwUWYBtYu1C9NU0wD/MDHMi12dloYyzvc6ya04myJ71mChw5usrO2zepmE0NCEsYPbpR7UZ1sd/uMgpg0zfTvXdt5ALWFrCinSYLrebuKmJJCIXOFv4/lvq9MGLdiYkIGrkW7FbM0Pf547911jy9WCesehVqJpaXj9IcBZjlHvVjDCDuMohFGY4OSH0O1Slpr0JOTvNVRFLE47lkcSBTbN1dYd22El2AXBYWiyzAYH01QzJe4ee02o0GX48dPUK3XiFB0dhqUq3XyxSIqivj8xz+KZeexun2SeoXYD/AW53lofQrhWWzfXSUxIedqQu3y+hvvYOxhfBMnEaZhsTvw3l16KBxLMFctUs6ZTNdLaK2oTUxgOEWu3Otz6sgBSv0+Pzh3M1su6sxQIU0VQmbdFnuQ2kTIXcNik82tdXw/oFgsIaRgol6hVCgSBxHBoEmchIQqxrMkwrYZjIagNFKaxMIAYaK1wpASpTW2Mf7z2NxoMjU7R6lQxDZNlm/exC0YzM4tIk2DYDAkTgSDbodeq4tWkCuaRDK7sqZpDJYk8WO0SNi+10YIiWO5uMXxnWPSVDE5OYWUEmlmJhCel6NQKHD9ymW6vTapikmSGNu22bdvH1euXMH3fdJUAylCZh3zcDgkijI0wmhUIY73ckHfrSiZ7RNrKytcvvQOG+sbGY569wo+Oz1FFEW0dhq7S/UYP0kpFMo4jo1SCZZtsrS0xMMnT3Dm1Ekcd/xXvzI9x6cPnWF6epJ8wUAYFm6+BKaDFcFo0EOmCYY0SeIE13UQho1OddZV+imvnb9BFAScPLXGmbOKcq3G9vYa9cr4M28hDDBMtGmgBZhC4jiZvrewMmlpP/Dx/RFCJghidKixHQ/HsMBw8Mo1qvUR63cuZp3ybiHdk563beG6Dt1uH0Nk5Jr746kgjjKCTpoSBAGubT8Ym2SkvB/zIAAM00TpCJWmDEYJiRr/e/ruGhBLOPnYCa5dvshWq4s1UaZvAUIzNz2FDIckzSbd5jqqs0Xutsfv58uY+xcoBpL9XpFaewiJ5K9++DLGVJ65SY+HP3KWyUpx7DwmJm1++R/8d+hU8Pjjj/N//rN/ShAGlMtlEAbFUgHLdJibzmG7eQwrJs1LAhRYMTLvElkGEkUx5xCFbQrlMq5r0euPPzYxTIFUKnOQRoO2yNmKWtVjsp5jspxjarJGc6eJKcBxHM6cqFGpFomkRduP8FyNZxoIW5DE2eY+TQVqDx6WppEVWpVqtje38X2fZnOHoutgGQaBP0TrBCESTEvSG6UMAh/LlPRHQ4RWGIZFqDJCgolA6xSh9Z7c43NugWtXbhKnARWnDBIazXWUMjlaPEYShQwHAVEYYUiJZRvkCx6mSAmDMLv6GhKkIOfkSYKUIIyRRQs3Nz7D8pHTj2LbNq7nIk1JFEa8/PLL+GFCp9NCGgLIWJPD4ZCdnQyppHVmuaV0tsewLJOzZ88+QH9YXo5ScfwZb1a8MykGIeD8uXPcunWT7cY2cRxjWRaJSmk2dxgNhxnu38hguKaZWcNJQ+B6LqfPPMyJ48eYnZ4iTQN6vZCZMbOoTkwSSAfXtVE6zZbztkuqNXEYoJII1zGIkhApLUbDAV6xRhQHuK6BlhLTK5Aoi5u37tDuNIkjQbO1gevsAeIrJMKyQIrMIi9JiKNMHiIIAoSQDIcjFhb2MTdTI1eRzA9MDCERhgQtsd0Cbr6YwT5/ouPei12f4zjMzMzS6w3+I8NxKSWGlGBZSCkJw5AwzA5Z27YplUo0m83dg3zEaDQi5xWQOkNmDQJN3/8pxXkHwy32T5cxTJc47vPBX/oFhu0Wb1xbYXhjmbPHlrAnqtg31zA6A7r4nP3MZ5neP0WpXOS1//VfcrSc48JAM3NwEnPuKAQBj+7bj2GOD0n7+te/S8ErMzszwd07t1m7vcwf/uEf8vZbbwFgeQ46VLSu3UHEGukaKBs63Q5CS9xKmbnDh8jnXXpbAfdWNvGHNzh16jRnFmb38EQE0oC8a5LPu+w0WhyYm2FusoCR+NSLecrFAko51KYOY3izFNwpHCtl5dKLpEFAGPpYuAw6owzWVnDJFcw9OZQMtu5Qqs4S+T6uEPijEf1eD10vc3fNxDSNDL3gZ6L6edclCQP6qaJazCHCEFOkFC0bx1J4lo2X8wgrDske8ii5Lu3+EB0reoMOuXyBO3dvcv3yGi+/+CzVepXZuYNsbjZpbGwSqxH+cIibd+l3h0jbIByFeFYOYaWYjkDmbUa9iP7OYOw8rt+4zBNPPEEURnz3e89RLNYZDHwWFhYpOh7tdhtHGARhwrDd5XqrwcgfZgy+SBHHAtc1SYXB62++SZIkmKZJLldmONzLzDubn0ohabdavPLCX3N35Q5xmB3SURiRKsVw0MOQBp7rUCjlEcLANG3uLN9k6cgSpVKBjfUVRv0dcp7LnTs32Whs8++//N2xsrh17SrV+WmqkzmOHTuNFDbb29v8w1/9ZSyZIlBsbW/wZ3/2Fba2usSpIhj1sUybMAwpl8vZ4ZZqthur/OW3vsnBA8dZmM/j5sYvVj2/yfLVi0zM1Cm6JsJ1iGMBdg6hAoLQYnJqnl/7jX9Mvzei6pTJuQV8Bb//pX/Hd59/iUKhRKHoUZ87wsbyNYRKSFSC2MNN1bZtTp06xb3VNaIgfNC1p2maaTEFwQOpiziJCYKAUqlEPp8nCAKiKGJmZoYkSWi1eyhCpJRstBRvXOnx0Jh5vMvFe5R5JQYpSsZMz9RQ02Vmjx3h6tsX6aoERxWo5SqYfZ/OToO5Q0uceuxhGu1tEILt0YiVQYRI69RyeQyR4rgW4R4gaTfublCqzVGbrJEvFbh1+zbXrl3j5u3bJImmVq6SRjGGiLHyFo5jUalXGSYxQeBTrNeZm51iem6Ga9duUqvWaKYapRLardbYeWRed1mXZEtNuWAxUS1RrdYZ9jv0IokYgVOsg2GTL+bJ5R26nS2itMuhfVOEUUKqFY7tMBgOGA59hKGwvfGvX9v3bjNdn6bbbhIHI0zLRGlFv9+n2bXJ5/MolTIIIwQpKtHkTQvPspAKcq5DvVKhPj1B6vfJewVK5TLf+MY32Wk0xs7DtiSeY1AqFjCUSaoMotjFkUV2tjdZXelhyhybaw3iKCSVITk3jyMtXJHZYHmWQ5qkSA2hH2BIE5kamHtY4LpuNmq4dv0KhXyOfXMzlIoVisUyL7zwIo3tbeIoxhQpKg4JIx9JkkE8pSCJMremymSZ/Qf2ZbNp38e2JUqNP9OEbIaqlOKdC2+ztr7OKAgyFq9hoYVAC1CpwpAGuVwOw7KzBWmSIIQijgO67R3yRZfIHxL4PsvLy9je+HZbniOZn5lkaWkJ03QQSKanMyeYyO/juhb7F+fQWvOX3/w+qxubRFrgOhaRysYHw+GQnOvxwQ9+gAMHDmHKApVyDi9fGDsPY9Rn/dpl4AiTU1PYpok2DPxRQK1ap98fEaMztEsU0BuG2FM2/WHI9RvXMxKMihiNUkzDxNTZQl5pRSrGb/6klFQqFUrFIjtBuDt+yVjR6e6M3bbtbGGPYGZmmtOnT9NsNhmNRjR3mkgp2b9/P81OH8MwsQ2D3iDi5t3xSVzvavEOY40wFabUCGKi0CfvWpRNqL3/BN3RiJ3ukHW9SjpyEbUyZadM0a3TlUO2FXRDGNom7a0N7obnWZzMkyQhcTz+zNuyXDSamzdvcuDAIpcvX8b3fSqVCoVCkUGvT5iEWHmXhcVFbNumM+gxuW+WSrnEKEg5fPQId+7e4fjx4zS2t7Ftk7m5mQeb53EiSRTSkRhCo5OQxf1TzE1P4pUnaYcSP0npdzWzpiRobWE6Fl/+ylfod5vUqhY5U+IHMYkQ5BwbaRRIU0UwSkjU+OiK44cOEIYhjcYW4WjERLlMp9clDnx22j2GQWYCnWiJa0jynsHiwjxpFIFKKZY8yhWH+Yk867c3kSZsr3V45dXXCEbjd5qGFszUSzieJPBTlLbwbIOd7Zi5mRr9fkDeMrENhek5DGIflWZwK9MyGY1CklihlCbWijhKSITCclxMe/yv+nA4pNFo0Gq1MU2T93/gfaytbvDKqz9iMNzB8UCREMYBUQhBEFGpeng5m2p9kiuX7mDZNo5jMT8/TS7nsLOzg0o1nreX4q2QAjbW13n++8+y02wRBBGWaRMn2X7DskwOHDjAxMQktm1xd+0eo+GIMApxXJs4CkgTi9DXRAg2N7bw3BJPPvn42FksLc4xUatSLtURRg6tUoRQWdNRzhPHIcNRj/edPYs/iPnaN/6CVm9EFGbfQSklExMT6DTl+ImjzM4sICjgj9boDbY5cGK8PGyVYKQxOkkJfR93chKUZtQb4dp5Op0uuZxLGqfMTk7R2trJPFYNgetanDx5lHa3hdaKcvkQ/c11hr0OGgj3wDg1pKRQzFOfqNNutRBpikp3tVIMG1PaYGgsw0ETkyvleN+TT/DyCy+h1zeI4pDGTgPDNDCNDEDhmB6R3yBmfBTQu1q8u4FAigjwsQxNJe8iYkWsYxJh41oWC/UiByYfA2nQVwmNjRVe/NZVbly/yqG/9UGuvX2Ro/VpttsDRoaFZ4d0Oo09qaRVK9Pc21qlXqvy+muv89U//wZhEOyKyGjynouhIIxdrt+6iZCCMMg6+xUU1ak5giThM5/9LG+/dRGNZmq6Tn1ifOdnyLbUURJjWC55J2V+Zp5rdze5dvst4jTDfSepwpNw+tg+4tEWw/42jZ0mtcJ+DuybRa+skSiDSCmUJJsLCgOVjH8NXLm7QlqMiXTG5a2V8ri2SaPZpjccMvRDBNk8e2ZhhsUD8xw9epQwCBEoVtdX8IMy+2dnuXlrk/LEBK++cY5ebOC44z+TA1MzaFLCMMSxUrp9n4JpE+U0SWQyXStRreTZ3pJ0+z6TuTxBKOnFATnHwRUOvf6AxBbg2kRRRJKoTAAsHP/7MRj0OX/+PL1eD4Ti29/+Jjdu3CaJU+qTBTqNHZIkoVqvMDezyNzsEs8+/zWiOKLV0igdYZg2O80NPM9mdr7GzFyVTru7J20TiFm5e4/f+a3f5fyrbzPsjjItnskq/8Pf/yUeOX2aYqXM1RvXefvCBa5dvcpoNCKKM2GypaNLlMslXNdifW2VXrfH4uIBvvCFn8f1xl8kI7vMTMyALqKV5Mb1qziuwWDYY6Y2R7lSpFyaZNDt8pEPPwVo/v2ffJUkUThujpmZGUajEZ12C6VDut02aRxw9MgRhr3xi9Vyu4MbBFxe2+CFF1/CdRwcx8U0LOb2zTO1sMjU9AQ67vJb/+y3+a9/8e+wE/2IDz3+BAcPLvDCK28wCrqkqaJcmuL9H/0ocRRw7NhxGpvj3xANlTIzPcHDD59g5e4ycZBJMQM4VoLjSDzPI4oihMyxvHybVrPN6TOPsbW+xY5t0Wg02Ax8phcOk6oI4gZ//4tPkbR+Skk6rXYPgSCfs1EGyGIu0/AQApUmJLsLBB2HJEmKMgzmZ+skcYWZqRI7zQZCnESmKSdPLpGrTuH7PkEYQzL+y1muVNhqbrPTaGIYJq5j4dkmuVyemZkJTCOjtCqtcWwHjSb0g2yWF0X4UcD5c+ey4v/66xQLRQ4emCEJA+QesHEZLFARxiFercDqvS3urDaQwqDgWUAGGzS0Zn1ji2rZ5qHDM4TDNjvNDU4/dJzZ6YROP6DVH5KqBKU1w5FPPl8ZOw9pObS7HfrDEWEYs9PJ/hynGqUFUmcqcGmc0uoN2W+4aNNDOCaubdJvrnLkyEk2G0Nis8zWAGKjQD5XwjLGP0Qy6a0MHRInPoOuT8GzSaIRUhvoOGF9fYM0iun3upTdKnnL3sXMJgjXwjQMfJUQGyDRBGlEEka4e3Dldh2XIAwzGFwSolSClALTMjJcr7QwHItcLoc0YGpyilzOJV92SBILITRBMML1csRJiCYBDbZtks+Pr3sDilAfXbkAACAASURBVO995zu8885FDNvgkz/7SWq1GvsX97Owfx/VepX+cMDN2zdZW1+l0+ugdYplZQp6c3PzFIo5iqUCnusyPzfHwsICjmti7+Em0ktLRIaNQwxorl99h8NLh5BKIw2BYUgEAkVEvmDw2OmH+eM/+QZJGuGIkHangcBgYqKObcxSKkxRyJnoYIihxr8xD8OIIFVEQYoQkKqQJI4pFktsbW8wfPkVpJQ884mPsH57nauXrvHYJz5EfXKS+tQUExNV/MBBC40SLsJ0iYOY5ZV13n7jNeB/HisPQ4Jp20xPTVIoFGiMmrArB4FISZKIKBLEcYwQFoaw+Yuvfo0DBxZBK1IshJnDcvOATZp2cXNtFioWvf5PKc4bLQmiGEMAtmAwGJLXXkbyQJIqRRKnSEFmKECSoQpMi2qlyGS9wtLBRSLfZ2OzSRiFePk8XtlD7AGBdeqRhzhy9BBhMEJKSavZIvB9CoUClUo508X+CedzIQQ6TQlShR/GLC/fpbmzw9e+8jVs2+HYsaOUCy5JEiH3MDaRMnNQ0BoM06HT9pGGg5Fmoj2maWAiCUY+oyBlq9Fl6YDD0cVZNrbbrK6tYlkeSikCP2AUhZm+QwqjYPzDbH17B+mWCPyQWCm6/R4Kg1whT7/TI93VYLFdj1avzyBIiDFIpUBYFqbl0RvEnDv/OhvdET0/pFYp02y3sfaA806NXdZpHBKnMa3mgNJcjZxjIcmYnjdWGhTdHOVCgXzOy7TVEfgqIhRgWiY5LRjGEYZpYXkGqaEx1PiHSD5fpN8fobXAtfN4TpHRwKfT6TI/dwbbymHbFkorSuUqM/NTTM8uUCoV6PcCNqwdkiiiVChnC2etCYOAvAvmHlT0QHP5nYucefQRTp48zSefeYZisUin3eIb3/g6K6t3cb0c165eYX19ndFwQD6fQRonJiY4dPgwlUqRYjFP/pFH8GwH0zaQUmMY47/6azsjni7mscwUw4DZ6UmkUqSJQql4l4Bk43gWBhGFvE0cRaQ6pVC0mZudpbnTYWnpIJ4zRTE3hZQtgkGXH1si/KdjcmKOfC5HkmiSNCaKBzS2N5mYqGJIk7XVWyilOff62+TdPMt3lznLx7h2c5nlexssHjpMECUordhptalXJzl86AiX3nlnb7sZQyKEplIqUizk2GnsYEiJYQgs6SCFRKQGKkoz8JOpWb51g+Wb18kV/j/23jzY8vOs7/y87/tbz3rPuVvf7tt7t6RetLS1WJZlSzZGNiYM2MNmoAzJECDMhEBwTTJJTSohQBImpIYloSpMpoAQB7BjBoQXAZYlW960Sy2pF/Xe9/a9t+927tl+27vMH7/TbcP8MefWVGlMVT9VXdVVXbf76d/y/J73eb5LnQPH7+batUWmpqbYPbeLi2e+wNuO7EQOB9TS8evHW1q8hVBUKyFhpFDKkWqHGaYE1qGiGkKWy7s8KwunkAJfFihZIJzEWUXoS3wVs+fAfkBggNXNAf3++MeNtfUVhLX4UoAT7JidRCqFAIIgLjGYziHkN4wPIEcFEUKF3H33PSPtglJR0FqLHn187HY0EpwjDALyXHJ1IUF6OSqShAQUaQrGYp2mUqvijGNxLaEzWKTqKwT+yCFGEYYhYRgzzDRxGOLXA/xtdJr4VQ7u28viWofXz75ZqrA5QZJlWG4IX0mMk1hrWFq6xokT9+CsY9AfUInqPPHkF9kadLiyeImJiSnOnR0S1kLybeDvIzxy6bBWIHNBrTmBUyH1WJFZgXSWWqNJLfDoDQdUqnV8Z0n8nEpssEiEcwyGQ7LAMkxzjO8oBNsiLWVpQRTGpXgQkjTR7N93CGM0cVRleqpGp9PBIdi9ay+bG6v4MkLaiGoUEoch09M72T23G095I6MPxfXr12+SNcYJmw341V//NZyBvNA44dBpRhQFvO/b30dRaDY2lmnWWwQ7JMIX7J/fRaM1RbNZY8euPVSqVaI4IgwVlDQulFlDb0O97o75JqsXX0UQQRBxYP88G5sDWlMTNBqNm3uewA+ROOKKIoxBmIA77zzBhz/0vezbdwAlBZfPXWS9s8b0dIz0A/xtfET27j5KnmdYW7DZWSdLOqSJ5sL5qzSbTXzfZ/fuPShf8dAHHmVto8fi4pDcbOKFdV559WXarTp56thav87WyjqTU1NUqxWOHBlf60UJh69gZqrFO+67k/WrZ0pOgFJUfYU/0jHJKSisJPYD8uESd919nIceupdaJcHzbyeKYwKl6e3bS1gUZOcsRRJSHTOPt7R4B6E3YhhJkAo9WjRp67C6BMybPEPgjY5i3FxuOisQBAgkzgoYUcE9IWjXYpq1cRUBYHFhhVq1ilKCQX/AwQMHUCoYaRK4kVJfyXiUowJuLTglkUrclJPNsoyiGFCvN5DOljol21Dzk1KiPIPF0i9yJBrl+3jh6IhqwBXlw+IkOCu5dHWN2dYEQRAy6A5J0w7KC1jvrCOkxDqP4XDIdk7n0g8YphlrGx3S3FAUpbKfdZT+anzDdkoKSJMEKD+dlThka3UVjaXRqrIvaNOaaLF4LcWPA66vJGPnEQQBaZaQZhkmzfDkSHh/0CXXklq1QhiGCAqazQa60MRRSLXqg5Ssb24RhiHDJEEJQRQEaOvwfEVRbEPdcKRLAaW8aJIkZFkJ53LO0ev18DyPXTvnaTWanD59Gl955UsrAeeYmJgombPGokVx07gjSca/HsnGKn29Qb06gQoi1jbWyfpDskKTZBnD4ZCtTocojqhHktp0ix2zewjrEwShoFavoXwflKIwjkJLthJHNW5iTca4zIj52RZF2iOOJUluiKJpKlWLBdrt9s3rsrmZom050vB9xSDLuba4QrVaJc8TpJTkOiNPh3R7FlVkdLdW2HnXeHkURYZUEucUQRChjaNaqWOtodPpghAsLy9Tq9UYDsqF6SvPfwWLwzjL1uoyfnqZQc+xvJlQrTeZHbljtVrbIQsJnNX4QnL89oNsveMgSTJEKZ/JVkCtVkF5Husba/QGBogpcrj37h20a31CuYZQPiIxaDsgCiSKBlSncd74JfmtJekUKQ5BUeQ4p0iLjFxrwjhEFuVcVRiD58lyS0wJxhc3/epK4SVjBTpPkbLUBQkQ5dF+zLh06TJhECKlwmhDpzvA971SzN3lhGE4ojSPmFfOcWDfbmZmZnAWDIIkSTFGjwpmqYxgnbgpGzvW9dA5ca3s3K1wFAbMMKUoIHAJigBrJSLP8DyPwmp8L8KqCI3i+vo62Wg222jWSJIUz1flR2UbC1yDZG1rQD8tyHXZ+ZaayKWwvRgxGYwpUEIw6A/R2hH7AbvnplheXCCsVnBiSE0F+KEBqZloeqxdHz+POI7ZHJbEB+UHNOsBYAj84CatuNFokvfWmN+9C51mbK2tlVoSQpLnWYkEqNVY39gkDCICBLkVBNsYV4hRLkIIsrQYWZiV+iSdjqbf79NsNjlxzwzXFhY5d/ZNGo06w8EQ48rntNfroXVxE6Lo+z7tdpvBYPwF3eVTr4Gq4tqzNGZ3cG1hgSItO/D2ZLt0zdEt5vdA6Ft2Hr4HT+xhc5BAaGlO1EqirRQoFJsrA05f7uK3Z/BtwriUBE9BMkwRURWJxQ8UcSWi2py6yRpUSuGsROcKYxyHDt3GK6+dI0nKUd6lSxdL5q1Q5EXOZqdgIrIob/wxQVwJqcQxnhei9RTNZoUsS0iThIXFBYbJsJRh9TxCL6S3cZWzr76GxcMPQiYaMbvnI5aHA15ZXmJaSISQ6CLH6G3Ia/gBeTLAU5JGJPnwf3+cXCfgFFEk8IMQpKSfTjHsp1gt2VpfY2aqQ71uCOsVRBDjkgTT71DoiELVCWZ3YobjL7Tf2rGJgziOyq5aAL6HsZZOZ0B3axXPK2mkccWnVq9QiSLM0CKkQ0oHlAs5nBx5LpryOJn3SoztmJGlpaSnk6UNU762SeB7KKkwVqOkGonLlF/ZotB4QrBn1yTO5UTxBM1alf4wZauXY/IhaZ6hvOimOfA4UY1UKYMqINcloqMahjQCwcCb5tylNYrhgLfdsZfNzc3STDes0h0O6PcHNCo+QVChEsV0h33yoiBLNUGwvTySNCVNM66vrZFmOc7acvnCNzPP3EglcGTU3O2y98gh6rUq5y5eYa0zYGNznWYlYquzQZImnCHBuvEfRuV7VOKY2I/JBjmhl6GdQStBNZYk2ZC4VmF5zbB09Qqh79OcaKCdRgiJlCNCkRDsmJ0lSTKyvMD3A4ptQMEcjHxKyyaipE9XCMIAzyuRBK1Wi5defJG1lXXa7VaJsQ4CvvzVr3Hk6BE8T440wA2DwQApJYP+cFs+eZUgRBc5naXL9Pod8mGOFB5B4OMrC5Ei9GtMz9bxvSmePx3y+Wf+G82pndxxdDf5wgLXlq+SZCmPfuC9NCdjwnOX2TzzCnv37YcxJaE2u10ajTq5sThhyNIhYWWC+sQExhiSJEEpxcZ6ijACXyl+5u//z5w6ewUpck6efJW9++Y5dOgwGxtbTEzUiIMIkW8wGIz/nDbqbeIoLnVjJESxhzGlSNWu+f3k6ZBBv8elixd56ctfo9PtkjsFIiFLtzBFlc8/V0cbjZKGVrPNoJ+VJiuNmbHzuOF7i9EoX+GFASqU+EGEH460fTxJuzVL2xMYnbErayEQyFKICOs88EI8XcP1KwjRYugCMpN8a6oKDtMEKQRh4JfyoULgewoXhQyHBUVeYKxjmPTJ8wzdqNGoV1DyhvONKfU7hBo5t5f0a1Ok2G2QMPbt301ZjMrZuilKnLigNAuNwmBkJqBGxqAFwuX0ux3CUJGJAdYanCnAZhSFJU0GCJHieeNDsKJAlphkY1CU7hs6z0ms4PT1LfrdlPmpJp1uDwN4QTjCozriakx/WL5U2lmMNfiehxHlsnU7OtpSKnrdbumVKShdR0ZCO99sJHxjXJRmGdY6rl5d4OQLi6xcX2OQO5wL2dgckuU5QngYa9jGQYQwjgiGPmEcYiOHyTKGSZ9USQZbCZ7wMcbQbDZxRU5R5ASBh3AOKQV1E2Odo5sMydMCqRRBGCBkgNjGR6RSqdBsNkdjjhTnLFtbXTyvdPyOotInsbO1RWuqTXtyiuXlJaxz5EVKGIal3GeecuniVbrdLkII9uzZRbGN+9LauZd+OkAaQWodobXYfPRRUaWJiAgk1UoV5WICzzA5swcvnqI78NgaLqHcgFYjYNBdoxE3ue/YTjodhRXjm0JEoU+WpBinaLdq5P0eWV9Tq04ilMBhSLMc6zRCgpUeIk+ZmywFsU5fX8eZneA8JiZaSAFFVpDqlEyPP0aqVqtEUVS6tuNIsy69XhdjDJVKhSiaYKLVolqrc/XyRfpJXtqaCINzhiQrGOY9hBREcRMhFUkypFarbWsnUhRmpP/tyAuFTKbo9wc4J5je4VC+RlqD8Cmp/FDqBxmD0AXOD4BS1EpoicklJod+mpHl21C/3K7D9624FbfiVtyK//9jG0CuW3ErbsWtuBXfKnGreN+KW3ErbsXfwLhVvG/FrbgVt+JvYNwq3rfiVtyKW/E3MN5StMkv/sbHHIBxpWnpDYynQ9xEkiDA9/wSimPNTcGXwPcx2pSuFb6PHjn7AShRCp//ws/972MBsf74VedWTz5Om0vU995Hp3k/frkSvvl33jQx+eZ97s0/FqUB0+hfK+V8S6yxc4IP3TOeLcexrz3lttLyZ2UubrrpOKvBJEhncCYnoY/RDlBUc8MNCIeSPrknSVVGc8vRlRKjDL6x+NUqyz/wY2Plsdrpu292EvlmdMk3/xrZkiOVwFpd4txRCERJSLEWrQ1OjBiqtvTn2bdvz1h57HnbISd0gKckYRzi+aVSm7aSNC1RH1KVBq/KUyjP4vuqvB9hjBElOihPUwLlIURp2GD9AJvmvPh/PTlWHjuO3u7+1S/9EgfaE3hGcDkdcnhmjuN79mPxuby4yFMvP0+wPuTuIwfJw4wjR++k2+1z9o3XeeUzT7KlobZ7jhO338H66hrFMOXue0+w57ZDtA/sHiuPJ79yymmtyfMC0MRxSKEtzsGfP/77fPbTf4jN+8w2mgyGKfV6hemWjxaOrX7G1OR+du/ey+233U7cqnNtI+PA7e/GetBZeY2f+uj/MFYe//qf/ISTQZ2p2b1U29O8/OrL6Dznve99jJ27ZimZAIovfOEpnv/a11i9voLvRyVhSZS8Tkf5+NxoF40xaOsRBCGffvyTY+XxL3/nN13GyHDb85DCx/cDlB8S+BU838NgRr6wAuVcSe4TEoMmDKvUak3a7Ula7SYIyIqUjc4WWTbgw4fvGiuPcPqAa95+mF3H7ubOOx/kI9/xbryaz4XNISubBbqAVrWBkqXhg5IKLxB4WCaqAYGzRJ6HlIo80whrMcKRFobID/iug7Wx8nhrbdBGRSDPM5wrpVl1oUnznCDycZQwN983JRbclcVbCEbQPIPRhlSnBLUYo0sdXSHLF3nsEAX9jUtUvIs0uhMEEw8gRroafwWI675BULkJyhFl6caV/nlOjsgslMa72wklSiElTzpc1scagyfAWvAIMUVKkQOeQxqFVAGRMzAiKyld2p15uaY+tCS1GGUtMjcYf3xonBDir7i8l0Sjb3y1bhRzAQySISsrI7W8uVmUgOXla6RJQqVSpVKrI4Qq5THFN1+4MfLwHZ6SSAFOWbxQoq0rYVnKokbiUOAQ8gYOv2TDWl3gBeVLa7FIXZSazRYKW6Dc+GShXZMtDs3u4MDOGaSxNLIUszXkqae+yEavlLitViMuLZ7h9gO7ubaxRrN5DaUkni95x7e9i652vHrlAmloubC1TNFLGL4Oi8MNvvPA7rHyyIqiJCfhsEaQDE3JOlaKRiVAWo2UijCKsBYCpfCUwFjB5NQcP/lT/wCBYOHKVU6+8Cp33fduRG4QVJBufJp+tTWNNZLOtTfJtq5z550nWFq8gjU5g34fIRVCKt758LuZ2znP17/8JU6//jreyAJPCDHyAxrxBgRIIfCkpdgGNM45cMJiXIE1Jf/DGocnLdIDq294WmqCIAAhcHbUILoc5wv63Yw8HZCbCRrNCUI/YO+ueQo9vozDife/nw9894fYf/sB2rVp4kjy9EuvsLCV0OsXvO34cdpBwfU85cKVJXwlCCamaARN1vp9bDGgXomxRcFUHDJZCfFwhIUG7YDx7s1bi/O2fYyxWJujpMf1pT5vvn4Oz/rc/chxvFjjRR7aFjglcdagAjmiaRuEBwQlEzPFgCcw2qIxSG/8CVDeH5AOuvQqjiljCa2h8ILRl9qWhB/ncIJSEa6s0shRK2GtQzoIPNDWjbQ/BAg7Qm+OF14BgRE4YcliR5KWDEGDQGiw0mI9h5+YUp3MePSVwypwViCtwFLQKFJarRarWUZFG7LN3rYEkG503Te7bykBh7QG62zpDZik/NEffYrzFy9y6eI57js8xY/8wIc5ePheLiy9wIsvvsqlq0Pe9/0/wv79+/BKnvjYOQD40kN6CuzooxzEUBgUAiXSsrBLOeryHdqB0KXlWKRqaKNBKqxTSOFhpcNIg7RqW+7gv/ixf0R/aYWFYkDsSQY65/rVNU5dWOHyyiLXzp6lOtni2IHD/B+//3FOL1zm53/2J3E6obO5xv72TvYeOEj7wA5eeu1lvFZMUAnomgHXVhfGzqPb7428OUu96Eo9RLicZLDBwuWz+FLg+zFb3S2kg/ZsixP3HGZpvc/ewyeYmz/E2so1HIKVq4s0H5K0pxXnrnZJxnfrIwhjSAfkwpJtLRG7wxw/fg+eLynyHCdKvkKlKrnj9sNMzcxw7eplev0BgR9icaUlHt9oBOBGw7CNpstJBAaFD9YjiGsgLUIajBtinUBIhZOWAkfhJJ5XMqmF9ct/yRlym7LRW6GTdAhkwPyug6htKKgduP9+5vceotfpcfX0AvMH53nuldfY7Bc8cPxtTAjDVJjye5/8JJvDjEJrZLXFsYPHGAw26HSWOHLbbZw4ficV30Mph3CWuBKhzbeoDVozmColX0ddWTXqcHD3O3j+y68z127TT9fKjhyJJ3ycqFCKVAO6LItGG4TTOFl2IaEKEFqizfidZu62cMEUEuht9WnJHOH5KAG+KzC+BwhM5uh3OnQ6m1w5d4HlhQUmZ6a578RhXnvx65w//Sr1yTkeevQxdh04QmHCkazpeGGURRuNLSyhqxGaEJMbtM5wJsGXkgKJVgFKhEgZ4aGwTlPonKq2NIuCg8tdDt2/j4vnzjC4voEZpvhqO7rRfy1kedAVlGp4Tz79DL/6a7/BpdUtrBGEQtC9vMjXn3qRow89wGOPPcrXX+nyyisX+exz/4Qf/zsf5YOPvR9Pqm113oEMUJUYk5V05TCuE1V9pPToK4HOc3JtRhorAt+mPHDkvdSjBv31U9z/0BGsaiCDOr/78T8tyVwYLOC28aQ/eOw2Bv0+XlCKSnmBz9F9h3nkEZ+NoeXq5St85olPs2X7NCZrHEim+M73PowzBgV4Wpf3VTju2fHtCCSmKLDWYrbh6TnZblLkljzNiasRlSDl2qU3OH3yWS6deYE79u9kqt1mplWhXqui/JAd++/h4f3HiRtTXLz6JtcunGN1ZYm//WM/xKf/7NO8/uYVdFjl4YceGjuPiigw1RpRtYHJUhbOvIjVORMzu7nrkb9FnnTBaDbXl8rTYVThn/7CL7J6fYOvfOlJXnrhebRV2Bv3buRBecNpZtzwnECoWjm68xRKCPwwwKmSfQ0CMTQEjTr91U3q7QkKMwQEYRiXJCIEeTEg28wIgohc+Zw1Odbm3HfskbHyeOILz+BEzNXNRS5eugB5gQ4DqvUmn3nyUzyeGXbuPMylhRWW167jZGkKcur8SaIwQmYFL77wMn8SfIpvf/SdWCTD4YB/+re/j9mJcWWp3uLi7ZsYrTVIicMiXQQyYHJqhrxrCb06SA/rHEJLnJVIofADH61zrDZI5wgkN2eswkqUE9sq3o24jhe0MEJTbU+DdChX4Cz01hfpDDWLi8ssLyyzcf0y3e4mSXeL0AuJ1FGe/LNXaVZjfF1w7rXn6Vx5k+N33suDH/oovh8xLge6kD6F75A6xFqD0ikKi3Maaw12NLQxxoJnQBTYvEC7DJOnRGs9jlaaPLz7Tl49dZKp4ZCrK2vIMMb1t9Fa/bUQrhybOOfQTvDya6e4trxGQ1RInME4y6BepTHT4oFHH2bvbW/je39knn23v8R/+cPfZmVpmUIXeL7clit3yRxUOCm4oeCqPA+hPKTvI6wGbUZCSBanC2wqOXv+TfbstKytLbK48iadVBDGMc5qpDJk26F5ApGyVNv1sksU5UnPWo0tMiKr8IVhz845PvzDP8SV0+dYvXyFwdoKwmikEzibjxh7rjQJcY68KG6a3jaP3T9WHr3OOhONScJqDMIhrMYTjn5nk8P79nDboYPEYcDW2jJRGNJNDK2pecLKBBbFzh2zXD1zkueffQabD9nY2ESnOfsPHQQ7vqenEx5hGOEKjfQD4jgk7w/ZvPYm/bVrtOb2UGRDms7S7w9Ikz46rbJz1wwn7nuA06+8jM0zhvLGSZWR5MD2zmZFYcjyFCkcQgpModEmRIUeynM4Y+icX6B57DbefP41Trz3IZwqxe5cZnGBQPoSH3DG4NkAIaDf20Kq8cdq3fVlXjv5AktrC3jG0MAndoLJGjz2ne9DFxpnArqHptnqdXFW8xdfe46NXp/+6jqRX8Wkhg3P8Qef/nOUH6ECj7sPznPb7AQfecc7xsrjLS3erpC4QmCFQ3gKYWtoZ8F5SBOhM4v0/dKIwVo8JegP+yRpSiWMmGxPonUBGny/FFU3RiOxeGL8x0AWQyqRIu+n5NkmwXCVeGoP2go+8Ue/w5Urlyhyy0R9mjTZwDmNsoY4nqJeidnoDpneO0u6FSN1C3PmDIO1NZ6KfO5/6L1wcP9YeVhRjkxK9UBN7hmcLfBchm8ydKrxjMYVpcKgkQXGQm6GqLSPd+0ix47fx4mD+3jqD/8bc61ZrnW7mHYI3fFfzv9HjLpDay0nXz/Fsy+8SFIYCoYITyClQ0rN+x97lHfd9zb+2S/8MpnWfPjD303tUwFTrTa+75cv5jY67xuymjdGU1prjMhRQoCSGDfSlZCy/Gu9mPXNHsI3dI3i8y+dohAhwptARBVsnoCX4dkqchvSo8PO6jcWtaMziLW21PHQApIuB3buoBW3iG47xp65nXSuvkIgypk93ySi5mxpiWRdKRdstuFS/vk//zTf9p7HmJho43mKs2+8yuKV03Q66+zeMUskgTzBWUcYN4kk7JrfS+58pB9iU8nytatYnfDcs19D4FOthNx2cA/OjD9rVp4ijGNkoEmHKSKs4QcxMxNTLL7xLDrpMbXnEFFcRUmPYTLk9Kk3+OIXv8gzX/oKshgQ+B5z87vx4wbGjIyVpdyWkFtnvUOhfJwpSkCDcoTNKo3JFtVaiHCwsXSdxsGDdJc26A8GNNp10mFBNkiIpmOU5/A8WZ4uJeRak9kCGP96HLtjnv7SRYZn3yS2koFQ9HGsC/jYj/wIYSUm8CRTk9VSETQXfPCBBzl35SqffvJpnnv9Ap4XkRuLchavyGlVWlw5dw67EsG3YvEepF3yPB81pg7P8/A8Dz+0bA06VOIqmILNlU3On7/E5ctXybKMXbt2Ua02uOP2IwRBgBdapCu1GW68zGYb2hXnXvhLFpYuUQ1y/EvnqOZTsLxCEMUEpuDQvgM4F/Cxv/8R0qSHsJp/9gu/zNRkE4Tho9/3WKl8+MAxPv5vf52eUZjLF3jnVJVLzz8Bb/+p8RJxAmkylHEYp4kZ0F7v4F+7Cmur6O6A3maHjvKQ9RqiVmPoBDWbM1MMeH+xzrujwwT5eR6YbDOz+xCq2+OprS7N/vi39sY1vPF7S7lcts7x6//+P/DGmTdRUaWU4JSCvTtr/M5v/xtCya/gzQAAIABJREFUv4rvh/zjn/m7XL18mWarwbve/SAXzp2h13mYZrO5LbXHPM3wg5hGJUZXIlSgwHMIkxBKiQkDoqBccltrcZ5HWJukNTHF6cWvosMGYaVOpVIj6Q0wRmCsB75gGysR8v4mUspRdygBiS8FgRRUQ8HEbAMqbXrrWzRnp5GeJRrOkPe3COOAIksQ0gMcGIOzpfaMNdsbm3zuU/+ZJ/74D0p1RE9x/Ohedu1sc+KuO0g6W+g0J1SObibxs5gPff9HwasgjSXLNCuXL3B98Twm6dIfKLTOuf3IQXT3GqYYX6jLZEP89hROQrVRZfKut1OpNhBCkva32Fi8wOmvfI4DJ95NEEQ89fQz/Otf/ldU45BWpGiEjs2hz9UL53jose9hbXWZLM8xIzTSuLFybQ0ZxyUCTTpqrZi8Y9nq9PB8SRD6RLNTnPzEk3gErL54hpcvrZGs9sh0yo5D88ztm2fqjp0EEyAo8OJSP99jfE2iX/7Y3+Oj3/1D+HmGrFRp7Jxj5/QkF8+e4eTKBg88+E6Us6SewPMklZrjPTtafPCdd/GzH/ku/s1v/S6rvT5xHGKXz3DnwXkasYdn+kg9vufrW9t5Y8mLjEqlchPh4JylUqnw0ksvcc9d9+ArxV9+/gsM+gn9fh8hBL1ej3qtSTJMmZqaYtfeaSZq0U2tbSEtvje+W/rzL30VWSgaExG610MOznDx+uuIQGE7a/QLn07XcPbiCs986Ulascf83E6MEmRpjyxLcEai/CrdYUoRhLj9+6ksr3L4/ju3dU2EyUook2+YWtpgx+ISbmGBqdAniGN6Sc6lwZCNxSVEtUaWZIQSdk1UONBQ9K4v0MfQqkX4SZd3HDnEk196fluCUPDX4IG4svt2jqmJNpEXYZzCeQWey/FkzjDZLJUdUwu+z/7bbyMvLEeOHOWFl17k1Kk3eOCBB7aVgxkhEYTvIUcIhUD4HNy9j/NnzuLJ0mwLY4ASlYKEIKyCUHhKEkc+QeChfZ9CKJx15UJbbmO26oejwi1KxMKoxJSICQfCoqTj9//o96lNTTJRj5lMttC6oDZR59Ce2W9cVzRO6NFP2hJKNG4eQlOtVCmKgkDB3cduZ3KyhjUJlekdBLZgY2WRHXsOc/+7HyNuTGEs6CxFacPa6iKeMJisoMhgz75dHNw/j3IF3jakWD1P4YoEFfgI4eMHPmqk4R3XmkzuPozwfBZOfo215SX+68cfZ7JeQWCJPEk3L6+/tY773/FOOuvXef65r9LvDxl/WAG6sFSrHoWxIAR5kiEDS+EMvlVoXRAGMWtra2RZTrK5TqM2iYgjbGZYuXQFZxzBTIPAOSoVg8yGOC9EhuMX7+U3zhPX22TJCrWJci8x2Z4juLvB6vVVXn/9NWoqoxpE1OoTTLZq9NIeymgqccT3f/C9nDlzBl8ZiokNApmQZ4aL66tYa/mOce/LNq7d/+fI8wRrNcPhAM/z8T2fQuco5fH8c8+zeHUJCaytbmJvGPDqEi6VpgXr65tUKhXuSY7xgye+hyiKStd4odnO9Gx5aYHJyhRbEvpFj4rdIpjcyV0PnOCVx5fYMzFHcmaRtY1NXjl5hljkzM226Q3W2eolYA9jnWK526doN6mkA673+qwtX+Leve8f/4IIwBS4AjyX0766wMTSIpORYqJWYeP6GrVGRBD6rHQcmTN0ri8ThDFx5KNrOWevXGZalEubbmeZ/ceOYIocE43vpHNjRHBjPl1iukHhePD+E1y4cIHF5VWkdEw2qhw/coAsTYkCjdHlOEEDUaXCBz/4tzh67Ch5UeKy3TY6TRl4CE9hEGgDDb9CM6qRrHa5dnGRifkZjHKgVCkTLARWaKZ3zFNbnSSTeYkasJaJ6gQyh97WJjIcKbuNe1vkSAZW3rgeXolnlw5pc4QVDAYJr7z4LH/25JPcdvvtvOPAPpZWr7O4vMSv/NOfZdeuOaRwOFPC2m5i4LdRvJUniCuK+am9vOfR97B/3w5mptt0ex08EeOyFBW1uO2+R5ieP0hWaOJKg4ry8IRkZeEKRZJQq1Zotndw7NhhwpHedDIYv/O2zmAGXRQVvDCit3IFsWMvUVQhTTL6SUbQnOb815/izeeeZmPtOvU4Lo2wBUjlIUYmCspTfNv7P8j07Cyf/PjvIbfReytZLj2tMVgLQklym2NwJeTW83GuRCM5bRgMEsIgBSXwQgWFRCmPYT+jk5QqkWEtYGZuHrsNH8U//cQn6S4s49KExK0wHGg+ffINajNtKq0dbK1ep9Wqc/dtB0t3sEhyZXEBhGO6FnNw3wGCKOLkqy8yFxiyYZ9Of0BKyjaUnN/a4p3oFOEJwlAgbEoQwuxUC2UUv/Iv/gWLCyu8ceo8+eBFOlubVOIaSW9IkWVloQsMCDj56nN84LF34oA4ClE4nB3/f+0X0MuWMR3LjtkWSR5QizxsljIRT5INNXfs30+z7jM9VWfXzCRXrlwirlTY2Fzm0198lguXFmgYqBQ+XsURbyZ85TN/wtobp/iJz35mrDwCrRnmOf7mBvWLV7i73yWTKQfuuI3TZy+xZQYcP3qUSpKx8eo6dU/Qjg25GVKojNUI0mGGXblG4/Y7oGoplA/aosX4nZXWunSdB5yzaFtCu64tXCT0NvmlX/hJLl9dJB343HF4P3M7JojDBlLUEZ7lyy9+iUqtyt49+/CFZNee3YAZmW6M/1GN6lX8KKYqfJpRgzdeeIV+HBArCMyQQec69Z07wJTORkKC8zXN1hxH77iP05efpz1RYzKa4ulPPInJCw4d3EdiLZkeX4q1yPObYxOs5SuvneZ/+3e/wZ17Zrnn4F5OL23Q2H2Yv/z68yTacu7iZTqra5x68yxR6PNTP/e/8I9+7qd57NGHyPspwgkko05+G04pO3dP8eHv+X6+/4d+BisrDNOEICgdn3zKbZ+1duQYn5bGC1ZRCWuEXsjh2+7m4umXyZIh73n4TlaWr+BsxAMPvYvN9c7YebRmd2PTrdItKkuxdo3uuVU6XpW0MkdnbZUvPf00T3/2ceqVgMAPiSoVev0hWltmphoUVpJllgtnT3Hi7uPce+Ie7rzrbl576cWx8+j3ulQ8n8D3Ec5SZKUxih+FmEygrcDajLyXIHAMdYHb3CQKQ7ROqddq1Ccncc5DDxUaSa+T0O8sU5gc3jVeHi+fPMlsc5KF7BIFGciSOGT7Q/7id3+b3Dn+u7/z0/jRNHfv8ZifLLjrzuP0hgmD1LKRwT333s+73vUwoc1ItWZjkPK5z/wxp06/Mvb1eEuL996Zeay1VMMITwgaE00alSZCezTqkxw//DYefnDI1589zJ995nHWugNqUZu0P8BJSPM+u3fNMDc3SxSFSFXChYpeRhxXxs5jPgho7dpF4VU4duwwO3YfpRL7tNoTzL39fyRNS9s1bfp85Ps+xM65HfyH3/qNcklmDZvrm0hnKHo95NaQTGc0qjHFqsFsA8cbZwWdTKO21vCvXWJ2/z7ObW0gvZCLF64yO7MDkxd0trawxrF7boJLy6t4ecHOdszsBHQYMDnp05dV6rv3cLazgXAeXr4dRx+LlSPyi7UkRcbJV1/nL5/4HG9/8CBhtcK99z9EI5gsmWuqRAFZSpTu8tIKcSVm19wedKywIsBZg5UCa8bv8IQS+Nrj0PQMq2vXmZltE0uBtBkrmyky8TCDHC8OMdIhlabVbOLLOq3mLHONBm2vxZkXTlMNFHE95uK5M+zcM8f87NTYeUDZKwghUCLg+dOnefbsed574hCm10H3Ojz/1S9hPYEWjjTp4VcCaoFHYQxaScJaDSEDPC8oDURGsR28uSJj1649xHGd3tCRJTkSiVWlA43RpR59UWQk6RCpJB45Eh+pPOrtaWqNJkp5LFw+Txw65iZn8IqEyvjfdoTRVCbmsOkWSpSkOIfEGfj8n36SJ578IssrKxyZqqKtZrLdJPQDjIU0SYnCCkWaozxBrjVpkqCkotFq8fZ3jlkxgTgM0caRZX2CMEQoH6kUzorS4cqUgy0hBWEQEoqAYVbgiRCNpBbHKGkZDHooXxIEMTYTZElBlo+vKx5WYrzEI+n2IB2SJ+X74/kelThE5AUXzpzmyqbkqcF15vdM8/B7PkBhJEWhsZuXUckmymmmmnWa7Sl2HrydEw+8j+tb36Iz74fuerDEd2blUVJrDUZRaMH6WgdtCpKiR70R8r73PcLSeocXnnuBemWC5kSNRrPOncePs2t+N/W4xmAw5Nz5C0zXp5lsTI+dx4nZfUy+/SGi6Ska1SZ3HT9Eu1krl3bfNDro9Tvs3LmTWq2G7wesr23gnKMaSuYP7SHd2EQurZXMNiEwUlD44zO1MAXeMMPf7LC/WUdKS61eY2V5hd3zu5lotuj3BqwsrdCs1ZmZnqISLVCJYoTRBDIkS4egBGcX19m8NmTNZDgB2h+/SKSmXFIKQFjBZ//8Sf7gv36S7vo6jzx6N3M79uIHdULrj3w+v6ELYIFdO/czTIZIEeHwkMrDSQt4CLE9slBDCLaur9LrbiI9SZoOaTeqFIXFSzVpd8hsq01vmGAyw8G9txN5FWIvZu/0Hr78+a/QWdtiz/wOcBBXYtZWlmjXx3VsBG9kICulRAqP7qDPoYMzhIElXblGTWvSa1c4cexOnh126G70qDQjju+aZmF9g0o1oFqrkukC6f7qPsFuY2zSqIb4KkBIhS5SsiTFkwJjC0QcYUzp+zocDtjcWivZlzNVBD5e4OH5gsl2m0atTpGl7Nk5w7DX4cqbZ8iLbRhl6xREm7A+jXIF2lgMATLXHN67i5dqIRcvZxzeewd5b4O7Hvtunn7qabb6QzKtcQj6w5TQ95mdnQMHeZGTJANCf3vjvULrcnRiUyq1CIRE6xyQo+ssEJ7COUujUiWoSsJaFYlHf/M69XSI9hxJnlMXpXtWkiTbYliSCc6feRVJjil8lPBKXhua3Pg4qTh18iRxtMDWygX8Rx7GPvJBOrkgH/T5xG/+BoOVBTAaP4yJKxUOHr2b7/joT2Ob47kbwVtcvJcW1zFYMj0kyYZkeUKmMwqRg0jxgxBHlXgyZH56N/vFYU69/gp33HYbWxs9fvxHf4phP6fX7fDys6/gBT6NiQmuD6+zdPoq38OPjZVHEvh89pmXEUJSSJ9/98//pxFErQznLHk+JPADoqgKWKqVOqu2Q6vVZma6RZoOSD3LcqCpRh5SKPKje7hjz3gwQYA0GTC9ss49ssJ9B+fINq5w1913cOrsOWamp3j2+Rfxw5AjR25jbWWNs+cXUUKWTi+tKbQrEF4dVZvhk088x1ajSWvHFI39s6jZ5th55FaXzuuAKVJ+9Td/i+FA04wjtroZeerwpcK4EYTTK8krN5abD7/zPWgNxhq00yU+XQiQ7ubHcJxo2xzTXWKdGvHEJNnGAjPtNsILSQ3MNafYMbuHl144yaPv/Ta+98MfIe3CoLvCjqrPv/+tJ9g7N8mh2/biVMSgsMQVSaRqbF1fHTsPM+IMCAT4ljdffInf+ZV/S7Z2mV/757/I4f27eeTYfpaGW7SkQMchHzg6jU2HrEy2+cGf+VluO7iLNOviI5G2LDzGmL/Shf+/xXSjRZGnaDtgkGwhyHjz7GkuXDzDPXffTxhW8H0frTWXL53iq19+mh/96N9jMOiRpwOe+sR/JO0scXjfHi4srXNt9TpSGLqqT2djfCedip+h8z5pCrYYEE/sIq5MEQQ1Dsdz1J/4C9q1CpXJaVSjxb69+3j0X76f+b172Fxf4x//w59n964aqxvr/NF/+c9IBMePH0cqSSbGH2fhylk+Kig9S7M+NivAU+hCYNFI5dE6OM/qpQWKzgYzx+4gnJ5EGHC5w/Zy4kBhJiKG6QCnDTKORrT08cJEE+w6dCcXzr6IajcxMsITklAptFPcd+8DhI1JnvmTP2Z6osL5c5d45vGPY51BC9h77AjZoUOoQKGC0lZwYB1f/PQfMDfhA987Vh5vafHu9lfo6i4FBmMtjgztCgIvQqmIKKxQr8wSIaGwOCuphk3uvOt+vvDUF7lw+QJry8tIDHE9AF+Ryxx8hSfGH5vsevu9vPDlFwiMKqn6IwywdSBFqe3R2dpianK23JoKiOISzZKmQ77y7Nfpdrs465BSceLOe5if24GUjl07xtOtAJBFQTXN2BVEzExMkngZCMHBvQdZ3+oglCQrLHFUIR0mCOlo1Oojk9UQ7SyD1BJWahS6KP04tSbY2cbUx7ePt0WOlZbl5SVeeParJOkAg0Lbgo2NTYzWJTa2KIuP1rrE6OLQtkCoGMcI14wu8evlABa3DQjnjprP4voQVQ3JtaEah0RhSC584moNzws4dttRvvTMV3juq8+xZ+4w9x5/G6ZYY6N/nUYY0owVFd/SGWakhSOMKjQbbWYa418Pbi5vHVobQqVo1WMK22ZrWGDDCsceeJCX/vTP0Q727J5ndbNH1RMMBgUzrUmwFuVGtPDRbPqGmfW4IW1GnvTLrlA44igkz4a8+frL+CZn34HDVGsNcmPpddY4f+Zk6XWZZ7z89WdYuLpIqzaSHPAKXNCg1xsiyImC8TteU2hkNSDb6qCHfRo7mwRxC5CsXL2MtJb9u3aQDPvo3haf+4Pf4yP/4H9lzpa49u/6ng+x1e3y1Je+xMXz5/jd//QfufftD/Ljf/cntrUTkUqRZDk2TTFANBGhTYErNI4M4YEKIqLWBLP+QbYuXkXGAbnN8JWir1PqlSl0kkNs8YKQ3KRkaY7cxo5o39Hb6Vy4hjjlIfsaLXMKJRH1KnF9goN3HEd4IVlvi/Uixa70uOorJDnDwnJpeY0ky3jgHW/nB3/0hzl75gy/83/+J7ZmZ1mJvkXd45cGKxQyxxOiZCuGE4QqxHeKZrNJmhS4ocT3QkxeYGyOyws2V9eQQrCytoLnO6TvkzuNHwXokUJhsQ3caic1RFEFrMA3OctrazibMlGrUUjF43/2OE888Tl+/h9+jDuOHAEsVhcsLFyiWo2IfJ8i7bGxOWCiNYGRlqheIUTg+dtgFOYFbaWYq9VwQlFvT7G8sAJGoYuceqOOChsMBkN8z6fabGA212lU6/h+SFgN6Q6vsLxyHc+lRDovlRhrDQjGH1cY7ej0N3j8Tz7FG6+9zFS9wmY3AWHY3MoptCRQPluDtEQJBYrclcxBixz17G5ETlGjxacDp7ZFodvfnubMpQUansTYgoYfIKVHZhSzszvJBglJ2kMJhycKksEa2A7oHmvr64SRpFGNCJxhoDW5VfiFwfPZFhnEao0ancSy3DLRblFrVli+ntGcqDI5N8t9j7ybYWuWyjNfJ4orsHGFlc0O+46+jWqtiSksnvQwugDEX1VnHDMaNQ+b98nSUsgtKxI2N9Y49cqLDFYusXh+D0FUIbWSxauXKLrr9Ac9lArwBVxb2SSOZ3FS0pqIyYqCVEO7ErGNGkEhYjyjiCdmcc05au19OGPZ2uiytbqEKjJmaj4+lqS3yUBV6PYytjod+v0hx44eZXWtg7M+03WfMxeu8pk//WN+4CM/zPTU5Nh5aKNJBglmMATPI54IiHwPawTDtEegPHwl6OmUdqtJfynCeoLBsI8nJYUwJDh0P0FUw1IyQUh0rrHbGJu0pyfonr+GcIK8MJiqh/B9nPKpNFsQVdG5IVKKQlsg49VXnsUTGidCMmspbMbqpVO8+eLXWVq8Rv/6EpfWVgn/b/beNEayLLvv+93lbbFHZOSetXd1Ld3V3dPb7OQMZzTkcLhTw5EokhqIFgQLBGR/oEhYki0BgkHZAGXTlmyLNGUYsmyKC0yKZM9Q7plpDmfrWXp6q+7q2quysjIzImOPt797/eFlt+RvkbDQIIE6QH0roE69d+O8e8/9n98/WPyj+q4Wb2kEK84ajiqb+3GUkGcFSirevPoWWimyKCWxBbnJwbX0Zj0m6Zj1jS7TdIKRRbkjD+qQpkgh8H0fx1m8WElX8vSlcyTzCCEV//fnvkQjn/DxlQkv6TNMxhOeevJJ/vE//kece/gso9GEaJ5xfGOLm3dusJdkZHlCtV4p2whS4gcV1pe6eO7ievMknPHEUpUzG2uYekAtWMIP6gz3htze22Z5eZVZXDCYTcmFZbs3YG25RRyG7A9GjPsJT773/Tz1xFn+E79KzWkzrNb49fkMv7X4BV1ShGxv32Jw0CcNI/7Tz36KrRPHuXV3jyff82HanSXirMBol0wUZHn8Nh0W3w3I8ow4TTGWQ7LiYYEyb5MaF4uJCvCOr5CZgnkSc/biee7cPGD/IMGpWpL5kDs7N7h48REGwz1u37xCOL5PFM4YT6bM0znzrANVn8pWQDjsUatrknFcTkYuGMaUMC7XdbixvcOph8/zxps3ef4Pn+ODP/Sj3N/dQzge3/ue83zk2ccwCLbv7PDrv/6/M7Ola7yjJVmeYg/dzeGwdXKEnnel4vLiN75MpXuM9eOn+Xu/+He58sqrnD2xwSc/+FE8Lbh95y6zMOLpEzXee/IDTMdDgtY6jz71NNde/jOuX79JHCdsbawg8xhV95gSEh8B5W+DUzTXzxNUK1hjufbytynCMcKv0F67wKWP/Qx3vv0Cw+EusnWaJ579Pk6d7JClhnv3RowO5pgkJaiu8qGP/jgXH9vnX/zL38B3XJY6nYXz8AKfhpUY18UgiIeDcsJa++QZJJMQv1qQu4rhbMYsHOLGKyWkLC+QviCROXNp6QqXeBASZSmVbgfF4iezoK4I5wMgQylNMbcYGRMnEa2zj7O+sclaI+C5lTUmwyFuEHDq4jMEnoPXavHSnz6PnI+5c/c2/8M/+x/L35KU1E6dpl5fnPb4rhbvteoqxdRilCQvJDbJieOQWWaYzsdYa9COpNAC6Tr41Qqbp09Q7TSRan4I+zE4SpOFMa7n47suaIXvL140b924yuVXX6MaVHA8n9Vjx9AabqtN7l6/yvLqBlEc4WiH/f4BjvbY3d+lyBN838PxfLAVhLAkiWV/b4+iENzvLKOVx8c/tZjW21hFVcbEJmXZ6zDY2+X6W29Rq1U5f+Eib16/QZgcUHV9nKqlP+phrSROclZW16mKgtVmnWg45OPPPkmvH/JWnKOSFHsE9U1uYHNrk7/+cz/N9GCfP/zd/5VwfJ7HnvwAW5vrJcJTS6LBGMezCGlIkwKtfGRQYR7G5Q5DlOPt7+y8DUfSeX/3xm3SaoC2BZnMubp3n6wQJHnCsVObNNseGPjEX/oUr7z6He7evg4iR2noDXsEgYvudkiUZZ6mODWPqU2Qqoo4AmXx7R1ykRdcvXaderXK1TevsL6+TlSAcBzywuBbg4nnFMay3x+wsrbKRz/yPezt9zi+uUZhSkqlEP/BzvsIxVtKxWQy46Vvvkinu0T//l1aFY/HHnmY+WSICDw0OS1f4epysCoJZ6wdq+HlBVubywyHQ27cuEO72cbxNUJKrLJkR+jx6toKWVYgZzMmwxEinlBp1VFhxisHB5xbbqIeeYybVx2WV0/R7a4xmQr2RxOSe/vEWiFcRcV1iIxLUiScOnGBIjwgTRe/oMuimCRJcLRGCkMeg+tX0H6AjAtMrknDCK0rmKhAZhlRlKBdF0drEBYjCoTnkCUhORbtOYx799Fq8feyWdnki3t9DCnN7hKPPvEBbly9Qjof09AZw3u36O+45HlKxRW4PjSrFbQTIHDYOnOW6ahHkeY4Xr1UuivF2SfeQ6vdXvy9LPw3/yOEMB5hMiK1OdkkIx5PybIEdLnbUZ6iqFq0q5BKMg3nnDt7DllYGvUGd+7u0mz5+MLBCSol/AhBHmcIsfis1rWrbxHNxyTRjMIa+sP7qELwrZcEx4+fotVOWWp38P0Kw+EMpeakecTTTz/BysoS5QkrxxYphfXIMstoPGf/YMwRujfUnSZZNkFVAlzhU/PabK6dZr+3jVsTNJoNdvZ7qDCj7vl0my1G0xTl1dkdh2x1GwyjGL9dY55LXg4Nr01ihN8CuXjxRrgonbG83OHYap3Z/Q9y5a2rvPnq65y/9IESiiQhiyZMR2OieIzn1tnYOIXrOphZ9E53pLAGedjnFf+hucUCEecSLQ0uhsxYplGCsppWJ2CcRLiuRz7L6HZXWWqvsnP3NnGe0Wo2aK8t4TWq9GTBwbBH4DeRwmcWzahIl6q3+Mf97Z7+dDbl6tUrPHrpSXav3+H9Tz/OvfGE/qDPZDImF5Y8ixlPZ9y+fYdqxWVjrUNv7wC7sVpefBrzDgrVGHMkgJopFNNJxOsvvcyZs2dJ5zMuPXSGteUGOYIwzckKixbmHZiYoiCPZ9h4TL1ao91q09sfstsbU234eEITVN2j3CMzu/o59PlPMh+GzHZu0Gp6JOOMfqToqIhOd4Wt4GlWVjZwlI/1XK7e7OMkI6pa49XbVGSCtZLdfk6zWuVv/vWfZ+9uj3T+ZU6e/KsL5TGfjDGy5EQqLVHSwVhJlucIR5VoZqsp0oIiTFBSEQQ+UimkhaQwzGchGQLHZIRYlHbp37+LHyz+cd/YPM/ucAC6wsbJi/zAT/4UB3t7zIcH3B9OuXXrLpsnzjEeHqBtxCwqiCan+d6Pvp9aq02l8zGiZEY0nfHf/de/grQlxOxgd5vP/OzPLZzHu1q8b77xOo2VLtqxNNoVBi1BICWj0ag0VhClLtgRHnlWcPn1N3j00UfJsxyUoNVssbRUI4wmpIe6zP7BPr7vv6MQWCQmI8vOzh0cx8fzPLy47DMJIVhd38CgeOmVV/ACTa3V4cLD51hptZFaH8qzOBztt+SZQWvncKhDHenHOQsH/NgHn2Lv9jY7RUahffJug05wmu3tXTJZZ2Jc3K011k9t4U5m3Bkk4Dqk0nJ1KeD5qsM8lYwHAuE2CJYqbDoO6gjSOIEt2x1KYRVsPnSW3dGM4WzOwUGfTnsJoRWTMOPNa7eZzPZ55vHHqFdqqMKQZQWpERSmoDRPRkGEAAAgAElEQVS2KW9+rVBHQn5mJkIkkrktQGikAFkF6YKnPLSqEoqQUX+A1AFBp4NLTqQN7lYTLSrEJsGtOchoi5qzQj0wRMl9PG/xndVktE+92mL7xi2cNGSyewebRmBzTDRiNtrn1//lb/LEhYdJojnTMObOvQO0FHzxC1/ifn+fNJ1yanO9PBlKB3G4tjnCBe5rV/fJijo3rt/l3/zr3+bTP/GD9O7cZP/+bUbTClmSUfcDWoEgigcI7fPkqWOMRztsX3+DL33l68RJxvEzJ7mzvccw0SzLJpWKj8mPMJhe5Ey/+9sksoa/9Ch35h5uPKQSWLLKFv3eHJtOcYMa7aUaReaxu3uf1PXxllukB1MS4aDiiDOrddKkwjBK6Pgpaf8I7c40w69WiJOYPM0psgJX+FhrKPwC4RqkKDn3WZ4gXZfx/n5Jt0xysjCi6tZQnkMRx2jPJU+mrHVaOEeQLD71xCW+/OpltBXYLGeWhqRFTp7l9HbvM+gfMNgd8Hf+7i8TxXPSOGMWhrxx4y0yk2OVouJXaDaafM/HPs729Su4juT7fuSvcOnpxVG972rxbq91MHWPaTxmnkSMxxOUdkCUDiRJkuBpj/FoSq/XI0lS0jRFCHAcl+lkinYLiiJCoEvIuRIgCswRnFKEVFgDs9mMLMtKRocxKKXKcXtbYIucWjVgebXL8nIHkxlMfighOzRlEEKW//5hWOyR2gTV1RZRAdVKk1azzUFY4DuSL16+wovXrqMadfpGM2w0mVVrjPwaUVuD65AJS64lBocID6tcFDM8O6UhBPYIOwkLGBTzxJBSoNubbF1ySOKc0Syh1SiHINrrm5xWNWbTIc22j5EJ5BYpIc8KjNAIodCyeMfSTR4Btm/tofZfSQSytMbLLG7gEUcj6tUarXaVLJuRZnOCSpVwvoePjy0M2KxknScZFe2hlCSJZ9TqLkk2WDiPYX8HUoOwBR965mneuH6doBYQ54bTJ47THwy4s9vnkUfOM5mM2d0/wHEqTCdzusurTNMZlYpHkaeEWYr2AoqiYDgcYozhwoJ5XL52j3ZzlVkU4gc+nXaded+j3x8gozmB5zHqHVA7tYqwGYlVpFnCsL9DGI6wGmwGSsNSt06lFlAU2WG7b3GKnhCgPB8/S0kPdnArK9RqkpGu4YRzhLQIr0q9JnCSjDy1JLJCtyrxkyl50KaTDam1NVPjkiiLFDEVVcVOFkcXB5UA7XtYDHGSYvKMPJGYw0lYKS0YiXJcciVQSNIwJNVQ9esUOqXIMwwFrszI4qJ05ckyjLv4kbkVOKBLzxIhBHXVRFqJspJzW6uHLTKHQpabGGkFaZ5QmII4z4ijmCLNysvOPIMiQwrInDpZcQTmzMJ/8z9CJLJgMh8SplNMkSDTnDSR+NUGruugEGSpIctybty4xebmBkVRAtzDMGQezqjEpSDekh0uxNL1Jk0X14sWuaFWbzAejwHwff8dFYAxBlNkXLxwHqks9WYdW5h3JrmUUiilD7lNprRDe3ugQ6oj6ZqbG2f46niIb31kb8YgttRay3zlYEZvfRNvqYNqNRh1qkyFoKhUmFlVDuFYg6802mgUHkGRYrVGCw+hJaq1+EVQYQqE8shNQWEFxmmxdnypZMpMJiRZQtVIanVLmvsoXSG1c/rjHpIAq+qlxlKI0p/00LoOjjDGB5iiBDdpVQLL3oZDJUmCYwuSJGY6m1E7H6B6gjwrcOoVpLLkJkcqSZ6VTkjNZgObGozJSPMJ4+niOu9GpQbktJea1DttmsvLJElMq92h5iuefc9TPJKkLDVaVD2PpXaHQXdCkqSsb2zQ7VZYXVnB5gUVz3+nbdLpdI6kimrW6mAS2k3Nia0V9u/v02p22N87QFlBJahhIsNoElHxJZlIuXXzLRxpyUzC6TPHDtkzoBxFVuTlUEoYEseLqyu0NDjaQ1UcdJ6SBy7DokU0meEoAX6Fmi9I5xE2KZjaglpd0ySjZz06yYRaReJIDyw0KgKRV1BhjHQW/7gXtqDIU/IswRY5pshICwNSIbIUNAjtgQtSCURuISk5/aljsMJi8gRTCGKbUnguQlhsnqO8I2x2tEDqt2FjUPLGylpgjKAwlszEOI6DtCVMzVUglKLiu7jdTrm2bckVT9MUYy1RZomjxdfHu1q8h8mYJElIkhzX8ckKRRRH7A12CMOQwWCALODkyZN8+MMfIgh88jw//GPY3FrD87ySrWxihIAkTZhNS1LhomFtSlCp4/o+RZEzD0Ocw6m64XDI3bt3We4ucfbcWVzXQ0qBlPpwsqvU/vK2R6M4HHkWpWnCUcwHVo8/wefSIRhLnMVEuSUtLOlPfZpAa8IiI8pSHEpLOGsluS1Kopsx5K4mtjkGhcoLtNMmk0sMlIevGgvnEWU5VitAo9B4WiOweNrgiIJBOCTMxpC7JHmB4ypysco48UAYrCitsOQh8S9HIIQCcswRTiLWWkxhyESOlBqEOPSqLHj49Ic5dfwhAifn4sZ5No5d5N9+7o/Yjt5ChxFSQYZCyAIKSbXio6oZSVxw9e5NErt4sfLqXZqBJiwgM5LuWo2iKE0gPEdTq9YR1qC90jvSmpy15dXSYEQIllvl2ipttwBrDn/cFq+yuJrg/U8+Tq+/Tb2+jiomHOz1qFc8hCxYW+4y7B8QxxlZzWUSp2hXkIZ7FK5GBYJazSXLCuI4I/BdilxijSVN80ObusXCr7ZRjkJYSaPdJBAp/d4cYz2a3Q6hcWE+x6oqvUCx6kRM1RLbsxn1dELn+BoHY4uRKa//4a8y3ruHdj2WGg1q5z7BDyyYh8ki0jRCCcCUBtflxWWC45Y9cN8LiEZDhLEEQR3POTT3KAqE56O1Kv1whaQZuEgtqfkdiuQIl1WCQ0tEDlHKJT65nPuQCCne2ehBOQtiTWmcXVhDnmXlsNahSbcxhiRNSTJ7FOjku1u84zguedyqgqMDlNIEwZyaSYiiiK3NLcgLqtVqaSCKfWd3rJRDELhIKanVaty7NwZhcbRLpVU5Urvig+9/nMFwQJqGpFlSOl8cysOqlYBKpcra+gZSSpIkxvf9cnhHlFhbqdU7UCB12AcvjCmF/ke4CKpEfcbJGFVYHCxZFCGK8pYcoIgiXGvRvkuaZghR9pXVIcTey52yXUBZ+EQ+RyqJUhWCdApn1hbKI8uy8uSAxFhxyOC2QIFQDkYqIgtCaoTvlW4xVpbPwxowFkt5ZyGEPHTstqhDF/mFw4KSpSpAiLJbLiwIUzCdTbl+/S4eKe89d4lZItAVzanmOaScsz+5zzSeAhZlHTytCHwDRZVWs0GjtrJwGrnfwMoMVwe4UuN5ivTwnVhHo6QHWUImyr6+lJrCRGRZqQ93HR+BQBz2tw3lh11pTWKPUDSd8gLS05osmtOoOUhS2p0qtapGUQdc8iLFdSv4QYUknkChmEczrD0clrICRytcDdYK8txQPYKe2LgBRRSidAmE8lxBuwE23qU/byKjEd0i5Kq/zKqaYcdTTDVg2cmo1ur0eylpOiNoaG5cfgWRZSit2LtnaPTHwH+1UB4Xzp1lHqUgIctLWzkpZNm/F6U1mlSauT8lz3KqQYBR5TpV5XGdPM9Js4w0ifGEQklFFsU4RxjSEaULefmOD3dwQtjy5JfniEOomda6dMiSkJtyLUghsUKUqiNjMUVBHMdoxwGbHzowLZjHUYYGHsSDeBAP4kH8+Ygj0GMfxIN4EA/iQfx5iQfF+0E8iAfxIP4CxoPi/SAexIN4EH8B40HxfhAP4kE8iL+A8a6qTX7v+W/bvIiYhAlZ8f91LAeLMaUvoStL49gMQ5qVt8rGgtaS+d4d0sJQ6WxQ0Zoiz4lMKZ375Z//0YW0Hl9/9WWbfudVrr74dao1n2vffZW69tg76LG3d4vVVpvxwRi/UkeTU6Qpu/MEV2tcAc9831NcGe4ycVzs8jkmk23C0Vu8emvKZJTQvz9dKI9//of/uQ38gGarSRSH9PsDsKVbuciy0ilISuIiI05ilBQ4WhNUalgL+3v7aA2OTkhjl1ZjHS0rCD1jf3iXf/DZ/2uhPJ79F79r1x2HJRkR+5L94AIKTR7k+MKiDyWRbqDwTM4n2xVexCN1A6Zxwrqx1JWksAWjrCAFssJiTOny/r+979xCefzP/+o3bZLETIY7WMD3unRaLerLp3n525/Hqy3huxX2b90imSrs+D7dx1YxRjGJoNttoK2L1Jq9/WvUq4rG6hmmewNsNuMf/b1fWSiPT/zIBRtFBapRZ2mty/7kgDzMqAQ1wtEuOBatLMNbBdpxaW90OfbkJbSqUu+sY7KEvZ03ycKYJBEk8xFJFqGqEUHQ5nO/9oXF3stHztvCGGrVKlJCkpRsD2MMneYqQpYQrDieU2/4zOchUnikSU7voIfjOO/oypMoxxqL43hMRyF5mjGbxgvl8am/fc7u3pmQJznNqk+97RHGKQIXV0tSk+MGNW6/sYdTd0BA3E/xPI/2skd7FHJGe+hRQqtTZf3hFrnbJs7rCOXyC7/6fy6UR+C3bbvugbAoAQJDkVtAoqQoJYBvD4fJUsqrHYWnS768QCOFRYjD9WktSimqjkY78M03by+Ux+9/e2R33niRL/zWb7KTWH7ms7/Aw+//MFGcl4qwQzUK1pSTy1Yi035pnv32ftlajClKizypSJOkRECLgM881V4oj3e1eBdpiOdJ6p4myykn6Iwhp5TpCUqYEbZAWpAYpM3BluwGWzgoL6CCxRY5kbHlJF4WodXi/5VvfeHz7P3eFzm4/DraFygEc+mQW4ObFcQHY9y0QJgZQmTINGYJF2lStFSMvnKdtcDh9JkNOs+8jy9/5Tle3hkx7CWcXFkccVmtVgjnEbW8hlYlXEtJB5BoYf6960oKRVIgMof7NyYEFYmUkskEXn39ZR55Yov2Up15OKIeKJaXmgzGuwvnYXB5XzWhe/cKk6Lg9UvrzAqXPBPkunQKR5SmAhIBTukan2Q5OrcoR5GSlwM6h8MHUgokivwIKNZoOifVGSvdJts7Q9a7FYbjHWJZRfoBicnI0glffP7LuJngaTcje3yFOJ2ytrRGmk+JM0uUJdTb3dJ2K4wYhAPWm4vPAUghqPkuVkCShkSDGdFBxCgfU20KHAeka/FbdaSniZKQRr1LlguicEbFabF75zp5Aq5NySQgHeJJion2F87D1R4agxBQHD5PjSbKYtIiRVqF4yqSRNDbH9BoNLAWZvMJQkiSJEMgcFyH9noHgWQ4HJNnKaY4gk1eqglaljyWRNMZUR6jlaTiGfZGDpVAYNMhK90WYZoR5gmP1Tq8vjNgexhxptPkgxeOkc9i8toaedZjfaXOwUwRqcVNQ6qBi5aU+n8MSiqEAFNYhDDkeVZOPQuBkm/L+cAiStbOYUEVQuI4iqLgcLAM5BH01f2bV1huL7HsOVz+zpf57lcfprW6RndlDWM1gaOxQgLFv5fUBp0SX40kT1OEo8EKDg4GaBMTTyc49Q5U/pzyvCfTIWquabYaKGWYTpLDLyKIwwEXIy0WgSmF1HiiwEiXzBTIIqG7tsJ8MmNeKBIhEXHCq9/5Biurqwvn8YUXvogznnJ/OkfjlacAk2AQIF2cGFzh4IqSwmaExnWqWHIcRzG1Hu5UsNFLuf1HzzEZ3qLptnnkRJOOXXzcdzwaI4RkOBwRJxFSaaSrqVYr5FlIkR16FMYJJzdPUHdW+K1/9k9pddr4vs9+r8ftezd5+PwZHFchVUGUjHGdOo3q4j+KxA04Jve4WBtx99YtnnHO0HMC7jt1vpl3mFMFLNJKHAVJliJFQJpmBKbUsOaiNF6wQpXSbltqWO0RdKtKufiuRpsETyusyWkFG0ymIb6oMI0mOHWXQX8PJQWjbpO21jQrTQJfkCWKSiVAxXWibEoWa9q1Nl6jS+a4C+fhVWuMelOcIkHVFOkkxqQFRS5IkchCYnNB0AyYj6Ny2jfLMLlkPhlSeJYiDplOYs6fe5i59DGm4P6VVyj8I1QJY/noM+/l7v077Az7LC8tU69UqdSrvPTWNZY6bc6dO89zzz2HMRbX9bDW0Gw2qBlJFMXM53OMsQwGfbCS6XRenqLcxZ9HbzCi0/Q4dfwcFzcf4/bekL3799jZu0rNN3RbLqreZv9OTLQfQprxwadPcOf6Pk+dP8YHnziP8Vxsy6DqKwh1CnRER2dM5eLrVGDL4RcpS/MPyrkLKwyFNeWgnDEoRyJlaXwhbIklFsIiMQgEUkiUEmQW0iSn8BX6CPr7PBwQU0WpjIubba69+EW+/s2X+PRf+RlOXXycZqtDtdkBLOJw/mR26wr1lTXmWcbunVu4nkZIj9l0xv72LRCChy69l+AIw4bvavH+tf/+HwIp9XqD+Symu7zChUce4+Txh3DQ7N67SxyNCOOYIKiye3/AwSRiPXCZFIb4vX8ZqTp4hUPlhd+hyGfg+njziF7v7sJ5/P2/80v84j/857wob3Bq4xhPPf0E23e32exusbLkY4ThypvX+fYbbzGeZyy1lnnqsUtsndxiOhkT6Spfe/0NHmosIb7xPFPPsPH0+/iZH/9Jnvu1f7JwHrNZhO97ZEnB4xc+hmXO9VuvMox2mKYhvt8kzRM6zXXCvCBNety8sQ+3+2hXMZvOENKwf2/CmSc3CXyFYwRxkqDs4kMYlbrHpQsfoPeFG8j4AOfFb/DISpf3tgQ/0a1zoNvcsV2+xjr7xkPoOpVpivEqnFERk/tDpIKkVmGqaiAtuSrwrEQeYWjpzq2raKdDUA9IE8Xly2+Q5zHGFJw/9iFMVOAIFwkE9RX0ez5AEC0xnvcxaQLWZSccI1RK02/gyBxlQIU95pPFJ+jcSpXxZA/d8fDmhrRf0D3XxYqCJI0p5pZcaNorGulowoOCO7cuU5ic0Z0xRk/wg4Rjpx5CLK/BaECSTtGui3UXZ/DUmwE3925wen2dpx67xCtf+y7LfgPf9fnM9/8Q1VaDF776FRrtFjqKmM5mVCsVtFYEnku15tJsB2RpxmwSkcQprusihSA7womorgTRPOOVy2/x+stvsNY8yaNnLvDLn/0b/N4//W/4rc9d4anz50i3e7xvOeCjFy9yRSh+9jM/wLqfoRrLRIXCI8KRPSLVIpYtvKDAUYtztIUo/UXtYXuELMMagwSMlWgpEcrgKIMSJWZBULzjZiQokFKRJykmByUdXGEIVDkluWi8/uIfc+GJZ9lcr7Mh1vjc5X1u377B//Kr19g4foJzjzzOY0+9j8B30EVOVRu2vII3vvY7vHL9DQ4mI2rVJp/4/k+y2ljiK1/9A/rTmHSyjxP4fPqpv7VQHu9q8W4EFep+He1IPCuJoowbVy+zHFRZP34a5UmGI8vGxknSNKe9UePkQ1XcUY+r33yRpfEO7oqPpyzVMyfJkxlOq43euUEvWtx1GZUzHA3R0nLv9l16BwckSULT3+bTP/whjIb5PCGaWxzpEU/mDPZ79Pr7TGdzVtbWIUpwmy5ZZlk6ucWjzzzLRz/5MeIrlxdOIwgC5vMZ3XaHlfYat2+/ynQwwKqITGtsmIIVyGpIo9YmGhXkpnS2T7NysSmpmQxC2s0GniNIpjFIS711BKh7YjCJ4fd+/8tcqE2w+T5G1RG149T29ziu7rOhWhR6xJveMSphnYta0Bvu0Lh3HXntJlZJvPYSyWMfJvUVjomxVmOKxYtVked4gSKbzZhPIwoDvlejWq/Tu3+T0fiA1VMP0Qx8PvHeZ3n6PZd4s78PSUSw0iWcjOkutdFulen+HaRXYTwYY22bcHaw+POo+PiBxmQ5cb+gql1a1SZULTtv3caVAfVWg6BSJwotjdU6p849w3Ryj5VVj7tv/il+TRG0K2ydeJQ3R19Ce4agtQSyv3Ae66sttnd2mM5nqJu3Gd3dZbffw6kFHA8juusbrG0d4/Xb18jzjCLPKRwXJQW1qk+r0UQgmE1n3El3yFKD53goYRFHaJss15vsTA/QGkaThCTcYfulyyTXb/OjH/8QTzx7iVTUsPKHSXa/S9dGHLgraAGh9GCe4FcCcuGRiwBjJJkxhKZAL07qLbnoWLAgkeQmx9gCKaAuBcYacgqkKA3By526wUiLpOQBKZMjjMXVEkmB4zo4UoBcfMLy4plVstkOjl/F1Go02wnZtduE4Yz9e9uM9veY7dzmAx/5AO1GiVa4kQckzWW6J2H42ktot0KcCyoIrJLlhHLYJ5ws7mL/rhbvv/TxTzKZhfSHU6z1EFvn2ahkTDJBEcXcdtbZP/se6HYItKbtKB5qC+5dfpmmqqJ1gX/3GsJ1aHbbTKYe0qvRXDtJpbo4AtWIlDTO0MLDZDEPnzzBaDjk/t1dvvXN7+J32gwOJgSei/Y0DdfjzvWrCEfSaLYIwxDH0VhbsLW1xvkPPcuHPvZBdD5H28V3eNZa5vM5rppw88ZVbt+5Sp7GGJHQWFklmwmSMCUSIYMQitjH9Vxms5DCFDSbTbRWTEcJ9WqAyVKkHzAP54fGyYuFE8fEvV02/YAVnTG4eQPPOviVLrJRR9gZuHPORddx7Yj03j2cZIyZ9KiN9zieJUwzSTZyiaoKdeoRErdCaAvUEXbeSIHrCka9XYzxEDZhPJpgEGgB43iCHrzBe48f42EzZvLCHxCvbDKdTelubpAkMwK/BlHGZLJHs32KcLrHdBRizOIf9yTJWG408aKUYZ7ROrtM0PLB5PStg2sslTCjqChMAusnNtCOxfElSlaoN9dwm02qjWWyLOHEQ48wn/W5P7nG/vbiND9jYtaWVxkNp6RZyNbWKrdv32awP+X+fo9We4V6u02rGrBy4jjdpS7tdpulboetrRXOP/QQwgrC6ZTn//RrvPDlb3H1+i3iLGE0WrxIHMymxDMBcUoRWoaTPsesZPetN/B+5qf5gXPr9PaHrDz2w7zyjRe48pXfp5qDNBlGFDi+i9YGaQxx5oIweCInNDnJbPGPmRSCLM9QFiwFVSwNYTgGPG4tVgn6KG4XFkdA0xHMgUwKqjkUSrJkJSvCobLc4TYJr4ch1vXJjyC869Q1joTtaZVZvoy0+3hakSUx1c4Sji0QvR3Ce9ep5CskymU42mbQOyj5NFlBxS346h/9P5y9dI7HLz5EhqFRaxHFi7ez3tXi/fKtCdZIXL/LklPgDLfZcrtUvYJXvvA8l2/ucP9+n6/17oIb4LQ3+NEf+2H+ze//IfXlE3zsmUfRrk+j6vClb99ka32d0WjA8bUVeq++An97sePG88//AViNtQ7WdTlIK1D1qG74vHx1h0LtU/VqFJlBOS7DYYbOPExsGMwm1IchSlU4Xl1lLlNmZsCL3/4czWt17t361sLPw3UdOp02g4M+r9sXWF3rULFrxPOEz/8fX+fW5R3iUcIoKVnJx4+fYXVti2WTMZ/NKUzBdDznzrV97t25hacdpHVJRII9AtDvZE0y+ubv0hR7fOnemKKzyhN71xj3d1neXKO14lKtVRCyzTl5C8FVjFAUVUOURUz3prROPIu3usnGy88RJSMun3yKa83ukTwbla1w/84OreVV0mlIYZr4TFGxpblxmmrQZv/Kazx18gS1RhPTahANh8zRvPRnL7F6dovt/m2UgkkYM4yvIVIJhSHKFy/e836PpeoSo2hI0HVY3dok2e0T7e1zXlbI04wuHje3J1x8+CInn/0ocbKHnOxx9eYrdJYfQnke9cYKWZhjba10Ss9exqrFX8yt7THjyQH1ehU/1/zxi68ipMVF8qkffJKL5y5x6cKjXHri4RK+9LZxsrVQZMSzPq6yFD48/CMf5m984gPc6E/45M//ZyAXB3XlUw/3fspaq8Nnf+JpXnrhJVa3VnnkyXOcdm4w/fq/w3W7xKvHOf/IeaZ7N7nx2ov4nRZpnjLLPXLhU6tLZru7VL06uA77sx7DyRGKt1S41rJByM/rGg/pFjY3qMEAKSOUZ/izSoWvzcesSYelapuPdpdZffMmbp5QoYq0FiML6jah/uFH2X36DL/0r36X9Ahcqtl0TjhPcGttWu1lvv2v/wgEeAp+6GyXcD7HlRnp9be4+dqrxGGIkpK9gwHz2ZTj622UnZPOc65/aY/MEdQ2Opx49jwt/wgOR4un/P8//GqbvIiRriZOLa16hccfv4BrEl77xgvYoI1bT1GzAOEEJMmcpYpChQf4cpW3tvt0Wx00AW/duImUDkWekZqM6RGqlRbgVSo0l1pUux1+8C//VbrLLWajff74d36buICaVyeZTtifTLFpjCgkJk/xtMZ1XIR1CecpHaC71EKTEw37aLN4m0Bpg7Ep7aUKtWqT4fwAnVXIBx5vfusG0TTB5pbcCrI85v69+xgEnaU2fgD3du6Rhimeo3B1nSQNKdKQzGZE4eI/Tt9Ktrdv8p0451pjk6C5RkdG1LMpxXCH0NQwyyfoPvoo5uAV6m7EWGxipUc+fQM8D1lpsHL8Ye5+/Tma2uN0MeaK6eIc4Xg+mu2ztvYwhpxKo4kNp0yGKQfjA7AJzaJJU7RIbQVrPZgNOdFpk0cxvm4xGc7oze+iPZ+15jp7oz4kllqtymw8XDiPbtBh8tYejq9x2lUObt7DH81ouh5hmuBUfZz1NZ5pfYDa5jHarS2SWZOVUydY7ca8+N1/izUpjlLg1jgY3McNGuSzKZX64iciqQOU6zEaTahZj2qjitYalVs+8uH38b0f+hgmKSiiGXnZ1EVpB6U1SI3UHnE6I09T6q5FE1OzCQ9tbXLt1p2F86j2oGkd5DTl5W9c4fh7nkIJ2Lk35omnn6IYDSkm29x96QVOPP1Jjm0d42vPf56T7SWSLOfK1bvYasHZx46hbMa4t4tfa9OttjgCZBFjCjZcj8/GDufDGUmrzo3jG9xptUjnMR+ejDkeR/yU16LbaHDtxDm+/eh7OFX9Nk+99BpxJUCZDC8v3Y2yN+5y7P2P4mpFdoSddzKbk4rk23YAACAASURBVCPJ5iPq/oSaK5iNcqQyrC7ViCsGKTVzo/GEQbl1pNCsKEXfZIj5FLdRR+eW/t4eXqdF2iuw4iL5EdqM72rxtkWByQ1KZWgJVc9hNuyxd+tNRklKUG2S3rtHPdAEQhNmGcoL6K5v4VbrJJbSeTnPkRRgcoQVFElM7hxhFWQWazKanRrL611cR9BqVpHG5a/93F/j+t199u71iIdjBpdfReYpMs+oKEktCNAojNVMJ3Pq3ToyTMnMELKYeLC42sRxJb7vIIRCKUERW9JhwWt/9ibRLCXN0lJtQomKnM8nKOXR6/WIk7R0H7IWYS3zeUqcxoiilO6NZ6OF84gzS19WuacbJPUVjK5w10BXRXSTMdlQkHfqOKMpzniAdefUHn4IGbSY7dzEqYJVVYbjGWnukhYO2swRQuAegentV9vMsxGikOTzGI+Q4XiCUIbhrV1qdYfl5gp2PiJmiItlSVa5Pj4gVopqrUKaNJFCMN7fw/drCA1Wzo9k+3XBbzE85jAYDSDM0Lmi1azRbtTYGYyoLG1w7NwPs3r8KerNLs12BSksRjhYYzno97j21nMkwx1C4QOK+WiIpUDni//kBsMJp0+dYXZwwODeLqQZ7WYLz/MoZhPSyQhrKD0ds1KlZYUgx4ASSC0wCSAU0nFwg5xqxdLWBeII7NGnNxqsLD9E1uiiVc5jF06xv32LyXCCe/x7KCY9prs3WX7ycWw0ZXrtJab728xWlmi3uzx6fs6N8atcHe7SmJ1jw29iAg+hDLcPthfOIzMW7bqsKE1yMObyLOMF0eSN5dPM5X28POP7MzhburfwncxyP2rjX3qS+/d3aZscYT0qsxgn8JE4yFe3CbQiMosvkMAztDwPg8TKggsnt9jdfZVMGdzGCsLzSGYztHCwQqFcyDPwKpZmp0O8t40TzvDcFnka4s1K8xRrDOoIqN53tXh7rirbJsohTxMaTckrb32XP3vh61QcB+UqCiF4arlDFsf0xxGuFgSVKp2VTQaTOYXJCcOccD4nz3LCMOWgn5KyuOToS1++zI8fvMUkBGfnZe59499xw2akWcrpeov3P/sM/rFjDN2Un/S7XP/6NbI0IUcySMeYmqQmBLW4x0o/J/mTr5PYFJkUxKPFi3caW/JMUpiUyfw2crLJnz73Mlcv3yUuslIXqhVFmmAKmE6nuG5BPI4oTIHWGu1IlpYb7O31cFxFPajhOOC5i6tNbo8O+Mb1Cbq6QdBYxl/fYDea85qe8vDoa7hJjGMFo9f+hLq4h+o2ifdfYTRKmfUu47a61GoF895t7s5yslTgjMaIVkJhjiDBSnMGu/sEgUutWUM5S7S2JEbC4JVr6GrMYGeH0+0tqsEGomLxVo+xls44UBVMGrK6fLw8iQUT8qTUWOcpDIfzhfNYDhqQ5KxUNmm3GmAt1VoFZUGd+35OP/39zOZzsnCArjlUhWaWOHguCGX5kY//Ta6cvMTnn/+f0G1FNO8xGs2IwhSbLv48+v0+o1GPzc4SYVYwSwzTu/eR2uH61Wu0GyucPHkKKwS+75WOUMKWTlEoXNdByxrx3BINJ5DGmChmfbmLf3txc4rzz1zEyCVkHiFQvHFjD4OHbJ3ku7/6C8hwyPKnfpr6hY8Q7tzk2puX8eM+X/ni5+nUagSbG/QGmqs7O+zs72B0gfZ8ar7HanfxdToYZ0zOnOTGL/4KYT/jN37jH7AsVomimCRRXP3M32Lrx36SFVlwrK146Etf4LXnvoOp13nl3JP8t//k75MXMW6WcbzbohjNcOYhjf/iDfb2Fr/QLqRilsSlmbM1PPHIcYp+j1fvzblx/Qqteo00K/DdpBQWSIWlVMpo1+NWnLORzNlcWiLutNjbH7A/T/nooen1ovGuFu8sT8mLFJEKbA5paginMdEkopXtsTdTZOGckAgtLVXfwRaCOI7J8oLNToOlVp100seYgswYpF8likeEyeJtgs98+mc5+C9/iXiagY44aTwGMiMWOfVBRnT9DU5vrmAnB4hbN1i/u0Pdk0y1oSYcSDKaUh7ytHO8Y6vYtmLmOxQs/vDzwuB5PoVRiDzn2uU97t3eJ06iUteuVcnZzuU7vWPHcchJoChAGFzPpVr1OXHiOFmeM+6P0J4iCBaXYOXxlFQJqqtbLJ+9QGVjg3hvm3HvFveFzwmRkIzu4qQDMhmVAzr918gHKfm4T0FOUbxEGApS5eLUWmh7UA5ZHeEWP5YRjaUNpDDsDO+w3FhlNJtgXctSpYo2MfnsPmrpONiUKElAe+RJSh5otFvF92pMx3dwHI2jAioKbt27RqvTWjiPtdVVHL9C1fVwXI2WFovh9t0dNp/5IPMwIo0ijI3Y7/UJo4x2p0OGg1aWNFecPf4Iw9NP89U7f8IsnlNt1gkn0No4vXAep06eYDo5YH1tFQrBfn+IlArHcRmOR/T6PTqdDn5QI45zpFLowAdbILSLFQKpFI52uH33LjaJCCoN8rzAHsGeznU75CgKt45DQYDEFBZXwY1b23SPrbDs1wn8gBhDGEY0paIeR9h6mz/+1lW2jm0wUz5eICnIwObUVxr0posPk1kEohD485hs0kdnPlHVJ88iHr+wxjOPP0w7nP+/7Z1JrGTXed9/59z51lz16o39hp67OTUnkSIpWaJoK9FgJ5ZhyJEDx0AAexED3iSLJBtn4QRZBXCcBM4iQWzHcWwntmBLiiyJMkVKoihSnLrZTbLnN79Xr+aqO55zsqiW4hgOUg0Dghqo36IXDTzgw617vnvOd77v/8czKcbA6VqVJMuIspxub0S0f0SajhjkKceXV7CWSwghcEoV1M7+1HF8v6dcSoEUNrV6kbPrC4R2h9C2wUiOeh3WFqpgMtI4Qbg+SZKxu3+AcgS+Dybt4xZDRvsHpMafDOXdxRHxh2vGEKWkaU6eA0ohxGSkGs/icKtFlk6E+EeOYq4gsXILOyhSWdlABAEPnmiQ5oabPVhZXiQsFDkcQLa3Q3s8/Y3Dm6++xnJRYC/XsUMbu10g7LaQWYK1sEjlgUdIC3Pofk5fBKTVBRj1cGxJsVZGZSPkMMXJDJmAvb196rJCXKkjG3NTx6G1QRtFkiZYjsve7UPG/XgySm3bE/F4pe44cWjyXCOkwLIlhom4vMDgB5Mb6jxXDEdjGgtzCHv6HZ5MI4qhS21lDW95g7RSpmg06ajDtdswr4dkrRuUrARlZchRilCbjLo5g06Co4ao/iba+IhgHuO42EZgCbDuwp3CC4pIldHut7GDAtp1iYlJk5zzi8dZWd2gPr9IftCnezSia0bYbpmDfhur6mFJgTIpJtPoOMcIQypziraPa0+/w9vd22VlaQPbcxglY+abdW7vHuBWFxHSJem1GI0TFueKJPGYARpLSqQ0tPZvsbTxAKu1Kp+9/+9y++BdNtUuVhjSnF/i2MmzU8fx1OP3o9I+Ihd0W4cYKQCNKzLev72H619mOGjx3IefIMoNwrKo1mrYtoPwDFgCVA5xwvde/S6Napn7LzwGriAsT9/VoOTE21VKl0xNRs8tKTEmp/HsZ1lZ9PjW159n/r6P8IV//+u8+LVvshKGFGTAluVy2J1Y6WnbQ+Q2hdDHD1zqdcM4nn6zg1EQ9aj+ye/gtw6RtoftOAjH4YOjPusvvYyJX0KQMQ5gQEKiFbFKCOI+4z/9AomcKFC0bu8gqj7uXIM413dlHC7uuONM7A8tCvUK9aUqdpbhuw7t3pjuMGaxPGDcH5OkChmU2W512R4MaW6scml/hxMUELnGCAstJEpr1F2Us36oybs3jiZTd1mCScBzHVr7I6LRmLIdIEyESWH9wiN4u9c50BGnT6zyi/VPUigVeOviZbI0Z7FRZ69teONalzgdcqpSJ+/vTh2HMRk+gsc3jkNo8WKpQ8P1qDRXGT/3Ud7tR7xrJfjH6zjnP4LHc+jnX2B0uEsbm5/4wAUGOwfsXLxJMUpx5xdoPPMEBy+8yXhveqNbZQxCGBzfwqSarSMFYYClItIoncz2SguVxEw+ygJpGSw7QAuBTDKk0RTnAmLdIiy6PHBhjWGU4DjT/7RWoUDtvmeoHD/PwAqwlcDUVlioNnl/5xZB/BY/Plfi8pvvEdsudF2EyNkzRXrhGk9WQ9aLy7zz7rtsPPtpilUHfbUFKwb09C1pc0GFfvsQqVPKhTK7vT2aC8dIBj1+/ytfJnAd6rUGjwaSLOpRnj/GzvuvsbC6yuFRl9PnHyXPEyphg136pIMI3TtivXqSUT59Oev4yfuwLIsoGYOCo16EJy1qG+fp9g7oto8YjQaszJ/n6HCbXBl6pQP6vS5Hh9s8O/8Qc+92iCsOS94iN6+/z9Zb7/PAJ3+KZDo5EQAefWydRtnHswPK1QKXrm5jMoOKDZevbyEth0G/y+n1VYRdpnV0wNJKAcuGohfiOkXiOOP9q9fJ3TKV+RW6w8ldRLc3nDoOI8B2HNQdD8w0V+gsRkqLUm2ejY/+NPUnEzZ390nnT1F+YJsX379OMtScP7fMo4+5XLn8Go6CC/evcXyjztqZ0+BmfOFPX5g6DoRAeT4HP/f3GERFvN/857gmx5Uevzsa8dzJM3zk2Y/RQ+KULY5eewV98Q/IpIOs1hj+9M/h5TG+yhEyQezvwpVrtG9cQ8jpP+7SssjzHGksLNtmME4onTvH0XCMKwNGrS5WOeDK0RDb83DrZUrNOo8/fR8/US0Suhbfe+Ud3vj225xsNiiUqvRlQKpydH4X3qLTP7m/OWmSTmyBLIXJNW6pQnX5JO5ujyceeIiXX3ieiiWonT5NeKyGG+UUihWq8wu0jtoTx/gsZetwzPX9ASJuYae7hMfupxBNf4u/O8o4d+IUe5lNkNscqy6g+znVjXXevPoOvXYX4Xh4Hni2Q9mr8Ei1SLdlOL6yQrcbkcQ5QVghzNo4hQAqdY6fO8H+3s7UcQRegTTro1RKPDRgNLYUCDPRLtFa/+AoJeBOjW0ycIAGLVOqzZBzD6wSpRHGKCzpYtsuQTD9mC2OTbBygkxYSDFxfNfCMBSGbHGdza3bHKgRuVvlVnGRsV3h2Kk13jnoImSBQ7qc8DWq7HB7d4fHV+cZeTZSCKQ1/QnAk5JjxzaojObIkpi85jMa9nF9lyiLGcYD2v02pcUmJ5p1Gmun6SUjatVjlOaOkWZjRoMeea6I7Ba2B9rL6eQxyp1+GsSSgijOGIxi8iSiUSmRWwXyYhOdxORphDA5/V6LztEhCEm/2yZXOYEf8J0rb/H4/CkqjRJNsUDRnsNxB5SLIePB9IszjjIGQkPo8PTTj/Gff+9PMKmg4oX4YcDe4RGLjQqFSgnXC/CCeVqtPZIkwfN8nCCjN0j44y+9ROimVIohUZyQZdnEym1KpJBozET0yRiMUmBAaUP/cJs0y4mGff7sj36H/YMWJ09u8IWL79PqRiyOFZ/73M/zlT/XuBJee/1d1h+aJ2x4CC05d/LM1HEIAaNxjhnELB6NWLMEAxUhpCDqJBy+9T7pqbPUVUJYsLjx4ksoCblIeCgao3/7tzCdFrLdo7e1h6MUheUayhg8d/r3NM0mloTGGMbjMZ7joqVk4/QanSs7+IHL4x97GulJ0juG2p7jkOYRWR6Tap+NpRLFB5fodA0r8yFRIlB5hhA/oq2CeZKRZym4LtIJudVKSYMKS2fOsbjgcvj5z5M7HlhFysfnsFKFEwYEniSwFbcOBly+3We/m2KNtihaEV6hQL97yNlTy1PHcWP7Jr/2z/4RV7dbpL0xVVyqH3FZOn2CP/w3/5LD3V20tPALDmXbI1iweOIzn+XmxSskSmJERDmwWag0ef3f/RtGgxbjd99GpIptOf3xS6c50XBEFA8ZdyRG5eRpioUkvVPjFmJyRFVmsnikECiVY3JDWLF4/JnzbJxZ4L3DiyzMNdH5ALDp96ffWeV5jghrWMLCuaPGhpq0UwXN4xxuXuJ22qe5uMaeaNJ1fc6snyRuXUTaATvDTfJCQlBxeXvnFk9xAWFLhGGiUTMlJssYS4Pje7i+z87edZQaUaw1aSzPEfdGaK1ZXT2B0RmjNGVhYZX64gq3Dm/Qa12l0+pRrjVJ4yH1uRXCuRI3dncw2fQtWFGSoLKJImKjuQACnLCK9IuoUYc8SzEqI00S0iTGGGh3jghLRYTncyPvMNfIOFFJeeH614hETL1Ro5BJ7j93Yeo42u0+y4sniJOYfrfFx597hm+/9CrVQplERbR2eziWS6nQ4Mb2LXb3DtnZP0AbxThKyaTD9e0dDoc9Rt19KpUqzzz6MEk0wrbvYvJVG4SQuK6HY9kkBnIzUft0vRBXwvzCIu9deotuf4yzPs9y2afi+Jw6ucyFC0/w8gv/ixtXL3LQPuKd9zb50LMfRiQj5hrTd4kJIcmHfdw/+30Wb13jEwc7fPeBOUxhje0sp/r+68z9xkWqoyOEFLTGGtFcoRn3eeK9y3Dp24yVwEsN0neR5RKjwiKdHYW+i+E6S9rkRpNnKQKJJcCoiEK5Qt9skvZ6JKMhBcfHlZBnhlxMPni+VyDTLl65jlc4IOlH5IM+QpSwbRchfkTLJkMhOP+Zf4BbreKaBNrvs2opThZLfPnbr3H/536Zbn9IdwTnz5+m4lhUKzW+8cZlLm61eKPvM9h7lbPlCHfdxWgHx7b52FMP4jt30dUwPOT444+xLy6z7/WoLVU51pxn49QG7X+6j05SlNEkyiWpW5imy8bPfRJ5/QEGvRFX3rrE2qk1LjzzNK/919+mMjgi6BzwlqjSO/Ho1HHE0QHJSFIKVmj3DiFPcS2BEg4RY8ydhD2RuJQoNemyMUohhc1nf+HTrJ0sMkw61GonyTLDYNgjDCxUNv1FoRtnGNFGU8EIH8eGjIm5rldvYm88ycu3NL90rsLysI5j+6xuPMLJYYUj5TJuOwTLEc0opC0U43SIl2vSJMWypk/eu3JEICzSYUToFym5PrZwKPsNHn7qYULbZ3jU49reLuX5ErqSYpkBvcPLlPwC0a5k9cQZ9vqbDJOYG/vXadTmqNcchnfRotfvDxGWTXNhEa9UIels04sNcSYZDYdgcjpHBywvNiiXPPb2duh09slVjC5UqaHZi9rIZAieTzgYowKX219/iQs/uzZ1HNKyee/aNkII5ipF/tZzz9JtD3nxpVc4d3KDjeOnuX3jBn/wxS/z7Tcv8urrl7B8F9s1BMJBKYWxJPOLixRra3z1u2/zFy+/zE9+6hMsHD85fRzSIo4nk6GxjvE9j0I5wJYWo8GAL//hf+Gxpz/MIw/dR9Qf0axV2DhW5nDYQYtrXPzm59lYWuL9y5c4vlqmXlL81r/9D1hKMn8XF8lGCNrtfX7n+S/yqVLIM26N07euoQ9eJNIjijl815N8WWXUtWatEPCPu7dYG4wo5hm2sBAYbCHwCi7ZmTV+s7PNOAOTT/8xGyUZWmUEoY/AZnOzQ24yjh+bp6VdssAjzscUnTKjUUy/H+OHEyNrx4DRERSK6OZx+u0tak0beyjp9Ab4/o+oqqCIxxxuXmXBO8ty2WK7ssYtq0r7xjWSjQ9gLayytLxIEMeMC5ILq/N88daAV/aGbF25xuD6DT5wqsFjpypIx0JrjW1ZWGiybPov1mf+zmd57Zuvgg6Yby5ikhEHV29jZTknT55FKEAK/EJI4Do0wirZUQ96fcxoxLFajTljwcEh3WiMN4gYZAbvgYc5c2J16jgkDs1Gg0JQ5sAZgZFoLclNTK4MWuUYoxDi+8lbTxakkVTqPo1mlU53gBcWSaMxpWKIHQbE45Q0mv4EsDjaRnnHGGmDzjKELqKRJEZj2w7W4gr7O1XCQBGES1jOEoG3iNtIsWUJV98klxGDaERYOEYYuqhUAYL8LhaFFBJjJK7nEyURZc+jXPbRlqE0F1L15lhsHqO8UMcNLcIQomii57l1sElQ8tnf32UwHlCdX8boMXkeo7KconamjsNozfxcjSxNyIZ9jLYZpSm2mMgXDUcDonjMeDwi8ANC32ex2cD3Q/ywiCNTdvd2EGPFk8dOs7N3E+VKRuM2771/aeo4zhxrcNQa4PsFfGmRGXjgwQf53sV3KRWLfOCJJygUCuy09jl1co255gJW4CIsxfX3bnPrxiY2FqSaBx85gzLHufzmm1SKS+weTV/eS9OJhrgxBktIeoPBRHffdQk8DxlrXv/Gl6i4NuW5Glpk9FXOwvlznFpdJBkXcD2f9aUmSjvUvCJBxSMZQInpu6KkcEhzwatKcUMPuRUYlsYW65ZiTUCeG6q54ZxtYWM4nRl2sgEHWqEEhFLTMIYyDlXX5ZsHt/hmtwX4d3VRmGaa7cMBhh5HrQGvv3qdk8fnWWzW6eYOdrGO5xd57/ou3/nee7SO+hRLPpVSgVKpQBLHRFqTjzMQLtI2DEYJ41gTBNOX9364et7DPtvf+BLZzUvMnV3nxqgI5Rp5eY3i8Qqp0WwlilZms3Wo+F5/j8H1d3B6e2zofU5vuDxydoFyMOl/llJiSZs0URPBmin5lV/5VX75Zz7OIx/4COsPPoyKx1z8+ovgCh576jlc20OZya7FxDm+EfzGP/nXtK/eoFwsEBZCRocH+EWP+37yk4RVG98OCJ/4GXrh9BorYVhjOExw3Zw8U6hcY9shljPEkhP1NK01QkwuNoWcTJlZtsXqepM0H5PmmiC0EdkYnYARGqk91F1MWB7rv0vqG665PsKvYfIUIzKkLVEmwQolxXKZQB5RLjj0y1W80OBbCRXpMC8STDai3R/SKG+gky5CpRTTLmR3MYatUtA5EsMgGuKEFWSe0u72qNQrHI0PsGyHsF4ii4akqWCUDCgGJcJahWTUxfd98OoUC0UQAeOsjRrmjEd3cUFnDKVCgTgeolVGsVzBcRvsAlkSobKYOBqTGYtjqycIiwWODrfI0gyEot/dQw+KNAoN5vKcvFwhEoqROaLUn/7C8vjqEkXHoVyqkGeKq1u7PP3EIxwetrny1hssLVRIxqt092+zsNDkwYeW2T3YxbIlS9Vl9ncPsS2bfnfIXL3A0uoaD933IO++9jaWvBsNnskHTUqLJE0ReiKOnSeKmAjbxIgxtEZtjgYDMpNTqBRQvZjN3i26g5S94ZBOcsTymSZuoUD30jVcZYOePlkZY8jutOe1M8PvJm2KKIoy537HIVNwmGq2sHGN5jtOznaWYVmTu5eK51EWmhIKJ+qwO1J0lIUXTEpD0/LmxZu8cekGWmlKxYBz55cphB7vX7/GIAXX5Aghef7FN7m920dryW5riNH7SAmWEOAI5qo1RqND4lKI7Vf55rfe5BMf/9DUcfxwWwXjCBMb9q+8x1ffeYtKMcbxXIquR5y7ZDm4YYmsUCKK+wyGPdLhIYsLDe4/fYL5mgStyJTA5JMHJIRCK/6PccEUfPH5v+C3/sfvkUsfYZXQCD71sz/DuNvm3Rs3yLKcTCsWV5Z59Y13UI7PL/ziL2Ibg1IZWucYkxNUC2S7B+ikRyYNS2ceQ4jpd5r7Ry20tlFaEuUpkRmitYUiJAgESlnkyoY8w7YsHNel1+tTqvicObfBwvwSuc6RQjDfaNDtdhn2hyRdSHp3MamV9ghVi0rLQbX3mA80BSDUUA8kgSNJlovUqwFPDW+yvbVJpXKSj9Ej7o05XXcZjx0qJYcPl4649vo7nFh7kAs7z0M/Aj41VRxHrQPq84vI3FDyagzTAVEcYUuXJI7IdYavPPrjhDzLGQ47nFg/h+N7WMKhJ0ZIy8KLbCKRYNKcwV4PQh9RmD5JNJfW+NLbN0miiGLgcmqhCvNzxDpGZTFZEiOE4cbV9ygEPsNel60bVxkOJ2PqzbXTPPTQHEtzVa57NttHXa6JIQ+fXcS9b/oywe2dLeYqTbTWjEdj1lcazM0X+Yc//1P8q+1brCwVEOk8XR/mljyM6dOs1igWKmTlER944jG++a1vc9+ZU5xYXcJxBEpq4qRPNZw+jlE0xrMcVBxRF31WrQ5XezbDYIlh0GGgx+wfdDhffIhef4vMTmiueuy3tzCqyJMf/jGebjbYvXWeL7/zFVQhYm61yutfO0Cdnj4OrVNAoA1gNLnRxELSyX02c4PBIJFIW+K4DrEjsUseaIMxgr6QdI0BBCbJyYXBNgqdKORdaM602x1+6XPPUW1USeIEqTW274MwvPDVN2jMLbB12OGwPcZz4fz5VS5c2JhMl+OgxhnFks/OQcyXn38B7RQJCwUOWy20Hkwdxw93553FKKUQjkU87tA9aLO4skSlKnGNxhYKT6Vk+wcEtiHNFW7o8sSFU9x//iyHe7uYXJBlCsgnY/JS3um7nD5Z7R4ccX1zl1pzkXKpiOVaSGzc3KcQeIzRSKUpeA6+lDiOR7FYxLJBGIOlLHKdIVxJNzrAcYqEtSJGGuRdnAASleHYLrlRxGpIqWbR62RIx0Jld+zhjMFxXaw73ScAtuWglCAaa7JMIYxi62YLrQSu7dDa6aOS6Z9HpC1C22U1H+MmXeaEngynhB5l12BrhXAD3KVz1DYv0Tna4uZ3tnClg6VtnLVl0vGYwdY2meyyNx7ywAd/ks6XvoYc34XWixfS626DCCn4ZSxchIoo1ookaUqU5mhLEgQOcZyAa7N3dEAmDEHgYiVjfNen1e8inSJpMsIOBTEpfqE2dRy3RvDKtS3K5SpiNObyXptHnz7FXNNFGI0lBbaUdHsHtNuHmDwjSRIsx8ZxHULPY3Bwkze7W1gVjW8tUe/0aJ5bwipP3xUVhg5KacgllXKdWA9ot/cZRRn7e/vc3tzEokBjrk61BFmWE9brBH7IaKAolkosLjY5e24dR4LQKZ4jOHV2jZ1bdyMINXn3HMdlpH3eGweo8hySIqGCPHNYLdRwA8Ned4ud0S5zJ8qcf/BpTpx5fKJ+KQv8xddeJdv3cTMX0+7zzOMXKFTvIgUZzR2nDzATOQAAJRSWkNiWjW1ZeZiAxgAACIZJREFUeK6NECClQmKh7kjDTubFJn8jpJyoEwomzmR34eb70Q9dwHYUo1GHLFeUSzVGSYRSCi0stnZaDNIxjZrHysoKjz56P76XgZETDfGGjdLw/o0O5bLH2vFlNrcPac6VMT+qfd5K3/GkTIdkcY9sPOZga59uq4NAIKTE9lwEAs/3EELy9AMrNEKHQfsAicRIgW2DEDaOA0orUtI7P+p0dPoZX//GG6TDAVLa2L6HKwU6TemOh0RJguM4lN6+TJRJPH/IV//4T/EdC8e1wQtxA4/14+ssnjyBH3oYzMSv7i4eaaVaJRorlFEERZsTZ5a48s4WmcomIkOWheV45FGEsG2q1SpHR22k9Ehizc3ru4Bg/dgS2zdvoJWkXq2RDHNMNn0c0e5VuvmAQtjAikfIko/t+3hpiNYRGsHuCE7e9yTtzoCofYBvBWipiQgYWoaDvT3mvRAbFxZWMMUm9dDnLmZjsI1E2jaVaoPDo0NKgU+G4LB1RLlYwrZ8lICjVgfbdYliTXewD1LiBzBfLtEbjdjrdJlruPTGA+bnGujBkDjuTR3Hpdt7+H4B1w3QWcIoVXccnwzGaCxLYEnDeNCl3z0iCAKUNqR5ik4jVK6Rwxy/ZLNpZci5JqfdAFFwGFybfqKwPxrgS4tSUCFVOeN0yDgaI6SHmBjSsXH8GFfevsz6+hpJMkbpMYNRD2nb1GsVPvGJj3NiY46CbYPMUSplcWWeg/b0O7wwDNCZwuQ5aS6x6qfwpEHGEZe2N7m2+x7Hl9ZZmr+PR089Srh9jbht4Z0skqeKza2rvHnpXf7b//zvPPv0/QQGXCtg8XiR7uH0tnCY5K+sc/MDDwVh2UgpMGjiJAFAisnwlDYarSeKi99v8RN/aZJRJpNkPi2loiQXNrYlsYTFIB7Rj8YYDcIt0D7oIgOHj370UZrNCjrLEcYlVwrbEqQ6J9eSXGV88MmHsZwSb79zhUcefAjf/RF10kmSBKFj0qSHzoY4NqTxkFE/JU1TjDGEYYjte1hC8+M/9hhnTi6SZjFxO8K2XIwR2LaLJS2SJEEphVKTUdVpicZjRp0Ol773CrdvXccgUHlOEifstluov/RSSAG21jQ9j43VFT707Ef49f/4n/jQMx/kD//oj/ALPnCnDxt+8O80jNOUXquNLW2CsoUXwWMfPsfhXp+t946wHVhcmuM733mFYxvr1CsVdg62mV+fY69zSHnexnEkW7dusjRfoVAoTEwsiusUgulbsAqjLmViXGcPz/PRcchQWHSli5Q2rmfz8lu3+PNv/QseWK1R9QTlYkLoW3zltbd4+90tHj1VxzaQKrj05nV+7/N/n1/92Q8jzfSaIkdH+4x7EeV6nzhN8JbXwFioVHHU28X1fdzQo2x7bG3usLW9z7mTG4TVkFa7y/LJ09zeu8bK8hwmiykVXPqtI1xPMBhNLwm7ub9PqbrE4cFttm5dpFqpcbC7SaNSQVoWrutRLBVpdw5oHWxzbP0ExeoixdUi554+w7UvvYkdBujRkEfmSlx5+yojMuximaw7/UlkkBnGakA/GjOOx2DFdNp91lZP89STj6D0GDvosXt0SLO7ABjidEgUR4xHijhqUyw22DvYw3FBqRSlNL5b49jqytRxjPt9sjzHsi28ko9KhkR6UrpcX1yhNueTx4p+PESkgodX78cqCa688RKXXvkW5bDBINvj4YcCdsdXeXlb0Dsc82vPfZzi2UemjgPz/9ZCz/OU79+N/9UVaP66//wbkCQav2ij8hQpJ7mjXAjQGGpNxTgpUK0XqddCskyh0olFoOc7GCRJEiOlw4MPruH4FsMRfPpv/xiNmn1Xnq8/5LJJgki75FFvoqKHmBw1jKFQLBIEAa5rMUoifvwjT/H4Q6fBqEnvs21hWRNzXq0Nji2xLBtLWuRS4XnTb/G0StAG/EKI7VgMBgPSNCdJsskPbe6ULATkQC4FB1mGHgxp7E12TvPNJkHgT8pAQv5fX/NpSzij0RDL8tC5IdMZpVIR2wo5PDjivofWuXX7JvVmgZNnV1g7Nk8yilndmKfeKDPXrOP5LoHv4ts29XodIQRpnDFqC/RdNPtLbBzboWALQltRCCW2a4O0JqPWKOYePcWNVszJZkCl6uF7IIXiM0+eJZAezbKFNhKlY+afOsWzT5yh5uYYe/paorADPC9BSQ/bF3Q7XdIooRAEkFswGmN5PiJ08AohayuLJJbHQqGOZ0m29jfJshyvWEaYPtIN0UGOHEExnP7iVFo2liXpdXZh1OHUhePsHN5ieXGFJI5AiMmJzbYZj/rE0ZjcGOywwtzcGu+q75LGGhUlVAtLGJlhFS2cw4hwcXr5hHa7x6g9olyoYAceqc4ZxZrdgx6eL/G9Ip2jhFxpBtEAx7Vxgyo3Nw8xmY3jCZSOGA4V1WqFwK+jlKJWq3J4MP1JRGUZtrSQRsLQwugM2wFj5ww6OY4TYOfgFGyKtoeRhuvti9we76ITwVNLnybpVdg87NDpZ5hwgdXGIq0rNxnfxbqdlr/2zZ9+Ofx/CQpFoiSZaPeIydqRUqCNpNYoUCwfoxT6pHkCQpCqnKIfAoIoSiZm6WYib5GMUxzLoV4P0Xn8g1LQNIi7EcufMWPGjBk/GtxFmX7GjBkzZvyoMEveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEPMkveM2bMmHEP8r8B16jwaKMnZCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "{(0.01, 0.001): 0.1}\n",
      "0.16\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16}\n",
      "0.171\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171}\n",
      "0.078\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078}\n",
      "0.078\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087}\n",
      "0.518\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518}\n",
      "0.515\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515}\n",
      "0.51\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51}\n",
      "0.427\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427}\n",
      "0.098\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098}\n",
      "0.098\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098}\n",
      "0.57\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57}\n",
      "0.565\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565}\n",
      "0.548\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548}\n",
      "0.443\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443}\n",
      "0.119\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119}\n",
      "0.102\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102}\n",
      "0.576\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576}\n",
      "0.568\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568}\n",
      "0.534\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534}\n",
      "0.371\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371}\n",
      "0.151\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151}\n",
      "0.113\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087, (5, 0.005): 0.087}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087, (5, 0.005): 0.087, (5, 0.01): 0.087}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087, (5, 0.005): 0.087, (5, 0.01): 0.087, (5, 0.1): 0.087}\n",
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087, (5, 0.005): 0.087, (5, 0.01): 0.087, (5, 0.1): 0.087, (5, 0.5): 0.087}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.087\n",
      "{(0.01, 0.001): 0.1, (0.01, 0.005): 0.16, (0.01, 0.01): 0.171, (0.01, 0.1): 0.078, (0.01, 0.5): 0.078, (0.01, 1): 0.087, (0.1, 0.001): 0.518, (0.1, 0.005): 0.515, (0.1, 0.01): 0.51, (0.1, 0.1): 0.427, (0.1, 0.5): 0.098, (0.1, 1): 0.098, (0.5, 0.001): 0.57, (0.5, 0.005): 0.565, (0.5, 0.01): 0.548, (0.5, 0.1): 0.443, (0.5, 0.5): 0.119, (0.5, 1): 0.102, (1, 0.001): 0.576, (1, 0.005): 0.568, (1, 0.01): 0.534, (1, 0.1): 0.371, (1, 0.5): 0.151, (1, 1): 0.113, (5, 0.001): 0.087, (5, 0.005): 0.087, (5, 0.01): 0.087, (5, 0.1): 0.087, (5, 0.5): 0.087, (5, 1): 0.087}\n",
      "r 1.000000e-02 reg 1.000000e-03 val accuracy: 0.100000\n",
      "r 1.000000e-02 reg 5.000000e-03 val accuracy: 0.160000\n",
      "r 1.000000e-02 reg 1.000000e-02 val accuracy: 0.171000\n",
      "r 1.000000e-02 reg 1.000000e-01 val accuracy: 0.078000\n",
      "r 1.000000e-02 reg 5.000000e-01 val accuracy: 0.078000\n",
      "r 1.000000e-02 reg 1.000000e+00 val accuracy: 0.087000\n",
      "r 1.000000e-01 reg 1.000000e-03 val accuracy: 0.518000\n",
      "r 1.000000e-01 reg 5.000000e-03 val accuracy: 0.515000\n",
      "r 1.000000e-01 reg 1.000000e-02 val accuracy: 0.510000\n",
      "r 1.000000e-01 reg 1.000000e-01 val accuracy: 0.427000\n",
      "r 1.000000e-01 reg 5.000000e-01 val accuracy: 0.098000\n",
      "r 1.000000e-01 reg 1.000000e+00 val accuracy: 0.098000\n",
      "r 5.000000e-01 reg 1.000000e-03 val accuracy: 0.570000\n",
      "r 5.000000e-01 reg 5.000000e-03 val accuracy: 0.565000\n",
      "r 5.000000e-01 reg 1.000000e-02 val accuracy: 0.548000\n",
      "r 5.000000e-01 reg 1.000000e-01 val accuracy: 0.443000\n",
      "r 5.000000e-01 reg 5.000000e-01 val accuracy: 0.119000\n",
      "r 5.000000e-01 reg 1.000000e+00 val accuracy: 0.102000\n",
      "r 1.000000e+00 reg 1.000000e-03 val accuracy: 0.576000\n",
      "r 1.000000e+00 reg 5.000000e-03 val accuracy: 0.568000\n",
      "r 1.000000e+00 reg 1.000000e-02 val accuracy: 0.534000\n",
      "r 1.000000e+00 reg 1.000000e-01 val accuracy: 0.371000\n",
      "r 1.000000e+00 reg 5.000000e-01 val accuracy: 0.151000\n",
      "r 1.000000e+00 reg 1.000000e+00 val accuracy: 0.113000\n",
      "r 5.000000e+00 reg 1.000000e-03 val accuracy: 0.087000\n",
      "r 5.000000e+00 reg 5.000000e-03 val accuracy: 0.087000\n",
      "r 5.000000e+00 reg 1.000000e-02 val accuracy: 0.087000\n",
      "r 5.000000e+00 reg 1.000000e-01 val accuracy: 0.087000\n",
      "r 5.000000e+00 reg 5.000000e-01 val accuracy: 0.087000\n",
      "r 5.000000e+00 reg 1.000000e+00 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.576000\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "best_val=-1\n",
    "results={}\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "#超参数\n",
    "# learningRates=[1e-2 ,1e-1, 5e-1, 1, 5]\n",
    "# learningDecays=[0.8,0.85,0.9,0.95]\n",
    "# regulizations=[1e-3, 5e-3, 1e-2, 1e-1, 0.5, 1]\n",
    "# batch_size=400\n",
    "# for lr in learningRates:\n",
    "#     for ld in learningDecays:\n",
    "#         for rg in regulizations:\n",
    "#             net=TwoLayerNet(input_dim,hidden_dim,num_classes)\n",
    "#             stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "#             num_iters=1500, batch_size=200,\n",
    "#             learning_rate=lr, learning_rate_decay=0.95,\n",
    "#                   reg= rg, verbose=False)\n",
    "#             YTrainPred=net.predict(X_train_feats)\n",
    "#             YValPred=net.predict(X_val_feats)\n",
    "#             print(YTrainPred.shape,y_train.shape)\n",
    "#             print(YTrainPred==y_train)\n",
    "#             Train_accuracy=(YTrainPred==y_train).mean()\n",
    "#             Val_accuracy=(YValPred==y_val).mean()\n",
    "#             print(Val_accuracy)\n",
    "#             if Val_accuracy>best_val:\n",
    "#                 best_net=net\n",
    "# pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "learning_rates = [1e-2 ,1e-1, 5e-1, 1, 5]\n",
    "regularization_strengths = [1e-3, 5e-3, 1e-2, 1e-1, 0.5, 1]\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "        # Train the network\n",
    "        stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "        num_iters=1500, batch_size=200,\n",
    "        learning_rate=lr, learning_rate_decay=0.95,\n",
    "        reg= reg, verbose=False)\n",
    "        val_acc = (net.predict(X_val_feats) == y_val).mean()\n",
    "        print(val_acc)\n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_net = net         \n",
    "        results[(lr,reg)] = val_acc\n",
    "        print(results)\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    val_acc = results[(lr, reg)]\n",
    "    print('r %e reg %e val accuracy: %f' % (\n",
    "                lr, reg,  val_acc))\n",
    "    \n",
    "print ('best validation accuracy achieved during cross-validation: %f' % (best_val))\n",
    "#pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "nn_test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.555\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
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